Overview

Dataset statistics

Number of variables96
Number of observations312749
Missing cells1286528
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory229.1 MiB
Average record size in memory768.0 B

Variable types

Numeric19
Categorical77

Warnings

rellmili has constant value " " Constant
rellb1 has constant value " " Constant
rellb2 has constant value " " Constant
traant has constant value " " Constant
prona1 has a high cardinality: 53 distinct values High cardinality
tcontm has a high cardinality: 53 distinct values High cardinality
proest has a high cardinality: 53 distinct values High cardinality
horasp has a high cardinality: 287 distinct values High cardinality
horash has a high cardinality: 345 distinct values High cardinality
horase has a high cardinality: 365 distinct values High cardinality
extpag has a high cardinality: 71 distinct values High cardinality
extnpg has a high cardinality: 80 distinct values High cardinality
horplu has a high cardinality: 96 distinct values High cardinality
hordes has a high cardinality: 78 distinct values High cardinality
prore1 has a high cardinality: 53 distinct values High cardinality
Unnamed: 0 is highly correlated with cicloHigh correlation
Unnamed: 0.1 is highly correlated with prov and 1 other fieldsHigh correlation
ciclo is highly correlated with Unnamed: 0High correlation
prov is highly correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
nvivi is highly correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
nivel is highly correlated with npers and 3 other fieldsHigh correlation
npers is highly correlated with nivel and 4 other fieldsHigh correlation
edad5 is highly correlated with nivel and 6 other fieldsHigh correlation
relpp1 is highly correlated with npersHigh correlation
npadre is highly correlated with nivel and 3 other fieldsHigh correlation
nmadre is highly correlated with nivel and 3 other fieldsHigh correlation
nac1 is highly correlated with anore1High correlation
anore1 is highly correlated with edad5 and 2 other fieldsHigh correlation
dren is highly correlated with edad5 and 1 other fieldsHigh correlation
dcom is highly correlated with edad5 and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with ciclo and 1 other fieldsHigh correlation
Unnamed: 0.1 is highly correlated with prov and 1 other fieldsHigh correlation
ciclo is highly correlated with Unnamed: 0High correlation
prov is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
nvivi is highly correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
nivel is highly correlated with npers and 3 other fieldsHigh correlation
npers is highly correlated with nivel and 5 other fieldsHigh correlation
edad5 is highly correlated with nivel and 7 other fieldsHigh correlation
relpp1 is highly correlated with npers and 4 other fieldsHigh correlation
ncony is highly correlated with npers and 3 other fieldsHigh correlation
npadre is highly correlated with nivel and 5 other fieldsHigh correlation
nmadre is highly correlated with nivel and 5 other fieldsHigh correlation
anore1 is highly correlated with edad5High correlation
dren is highly correlated with dcomHigh correlation
dcom is highly correlated with edad5 and 1 other fieldsHigh correlation
dtant is highly correlated with edad5High correlation
Unnamed: 0 is highly correlated with cicloHigh correlation
Unnamed: 0.1 is highly correlated with prov and 1 other fieldsHigh correlation
ciclo is highly correlated with Unnamed: 0High correlation
prov is highly correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
nvivi is highly correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
nivel is highly correlated with edad5 and 2 other fieldsHigh correlation
npers is highly correlated with edad5 and 3 other fieldsHigh correlation
edad5 is highly correlated with nivel and 3 other fieldsHigh correlation
relpp1 is highly correlated with npers and 3 other fieldsHigh correlation
ncony is highly correlated with relpp1 and 1 other fieldsHigh correlation
npadre is highly correlated with nivel and 4 other fieldsHigh correlation
nmadre is highly correlated with nivel and 5 other fieldsHigh correlation
dren is highly correlated with dcomHigh correlation
dcom is highly correlated with drenHigh correlation
relpp1 is highly correlated with npadre and 11 other fieldsHigh correlation
ocup1 is highly correlated with ducon1 and 24 other fieldsHigh correlation
nvivi is highly correlated with proest and 5 other fieldsHigh correlation
npadre is highly correlated with relpp1 and 9 other fieldsHigh correlation
repaire1 is highly correlated with mun1High correlation
ducon1 is highly correlated with ocup1 and 29 other fieldsHigh correlation
ocuplu1 is highly correlated with traplu and 3 other fieldsHigh correlation
nac1 is highly correlated with anore1 and 3 other fieldsHigh correlation
tcontm is highly correlated with ducon1 and 1 other fieldsHigh correlation
anore1 is highly correlated with nac1 and 5 other fieldsHigh correlation
mun1 is highly correlated with repaire1 and 1 other fieldsHigh correlation
extpag is highly correlated with extra and 3 other fieldsHigh correlation
trarem is highly correlated with relpp1 and 36 other fieldsHigh correlation
parco1 is highly correlated with ocup1 and 29 other fieldsHigh correlation
traplu is highly correlated with ocup1 and 30 other fieldsHigh correlation
eciv1 is highly correlated with relpp1 and 15 other fieldsHigh correlation
sidac2 is highly correlated with ocup1 and 32 other fieldsHigh correlation
sp is highly correlated with ocup1 and 3 other fieldsHigh correlation
regna1 is highly correlated with nac1 and 2 other fieldsHigh correlation
itbu is highly correlated with sidac2 and 9 other fieldsHigh correlation
extra is highly correlated with ocup1 and 27 other fieldsHigh correlation
factorel is highly correlated with proest and 2 other fieldsHigh correlation
situ is highly correlated with ocup1 and 23 other fieldsHigh correlation
ncony is highly correlated with relpp1 and 6 other fieldsHigh correlation
ducon3 is highly correlated with ducon1 and 1 other fieldsHigh correlation
prore1 is highly correlated with mun1High correlation
proest is highly correlated with ocup1 and 31 other fieldsHigh correlation
edad5 is highly correlated with relpp1 and 35 other fieldsHigh correlation
situa is highly correlated with asala and 4 other fieldsHigh correlation
rzdifh is highly correlated with extpag and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with nvivi and 6 other fieldsHigh correlation
ncurnr is highly correlated with cursnrHigh correlation
nivel is highly correlated with relpp1 and 14 other fieldsHigh correlation
ducon2 is highly correlated with ocup1 and 23 other fieldsHigh correlation
npers is highly correlated with relpp1 and 9 other fieldsHigh correlation
desea is highly correlated with ocup1 and 24 other fieldsHigh correlation
embus is highly correlated with sidac2 and 8 other fieldsHigh correlation
busca is highly correlated with ocup1 and 33 other fieldsHigh correlation
sitplu is highly correlated with ocuplu1 and 3 other fieldsHigh correlation
rznotb is highly correlated with vincul and 1 other fieldsHigh correlation
busotr is highly correlated with ocup1 and 31 other fieldsHigh correlation
cursnr is highly correlated with relpp1 and 12 other fieldsHigh correlation
asala is highly correlated with itbu and 8 other fieldsHigh correlation
nuevem is highly correlated with ocup1 and 20 other fieldsHigh correlation
disp is highly correlated with ocup1 and 33 other fieldsHigh correlation
cse is highly correlated with ocup1 and 26 other fieldsHigh correlation
rzndis is highly correlated with ducon1 and 20 other fieldsHigh correlation
ocupa is highly correlated with sidac2 and 5 other fieldsHigh correlation
horplu is highly correlated with ocuplu1 and 4 other fieldsHigh correlation
hordes is highly correlated with parco1 and 6 other fieldsHigh correlation
aoi is highly correlated with ocup1 and 40 other fieldsHigh correlation
actplu1 is highly correlated with ocuplu1 and 3 other fieldsHigh correlation
edadest is highly correlated with nformaHigh correlation
ciclo is highly correlated with Unnamed: 0High correlation
sidi2 is highly correlated with aoi and 3 other fieldsHigh correlation
cursr is highly correlated with relpp1 and 16 other fieldsHigh correlation
vincul is highly correlated with rznotb and 1 other fieldsHigh correlation
ausent is highly correlated with ocup1 and 29 other fieldsHigh correlation
nbusca is highly correlated with trarem and 22 other fieldsHigh correlation
prona1 is highly correlated with nvivi and 9 other fieldsHigh correlation
mashor is highly correlated with ocup1 and 24 other fieldsHigh correlation
sidac1 is highly correlated with ocup1 and 32 other fieldsHigh correlation
parco2 is highly correlated with ducon1 and 5 other fieldsHigh correlation
extnpg is highly correlated with extpag and 2 other fieldsHigh correlation
dtant is highly correlated with anore1 and 4 other fieldsHigh correlation
Unnamed: 0.1 is highly correlated with nvivi and 5 other fieldsHigh correlation
sidi1 is highly correlated with relpp1 and 25 other fieldsHigh correlation
act1 is highly correlated with ocup1 and 23 other fieldsHigh correlation
dren is highly correlated with ducon1 and 3 other fieldsHigh correlation
nforma is highly correlated with ocup1 and 21 other fieldsHigh correlation
rzndish is highly correlated with dismasHigh correlation
fobact is highly correlated with sidac2 and 9 other fieldsHigh correlation
acta is highly correlated with sidac2 and 5 other fieldsHigh correlation
empant is highly correlated with relpp1 and 29 other fieldsHigh correlation
ncursr is highly correlated with edad5 and 5 other fieldsHigh correlation
dismas is highly correlated with ducon1 and 8 other fieldsHigh correlation
ccaa is highly correlated with nvivi and 5 other fieldsHigh correlation
exregna1 is highly correlated with nac1 and 2 other fieldsHigh correlation
ofemp is highly correlated with trarem and 15 other fieldsHigh correlation
aoi_num is highly correlated with ocup1 and 30 other fieldsHigh correlation
prov is highly correlated with nvivi and 6 other fieldsHigh correlation
dcom is highly correlated with edad5 and 2 other fieldsHigh correlation
ayudfa is highly correlated with ocup1 and 26 other fieldsHigh correlation
sidi3 is highly correlated with sidi2High correlation
nmadre is highly correlated with relpp1 and 9 other fieldsHigh correlation
regest is highly correlated with traant and 3 other fieldsHigh correlation
busca is highly correlated with traant and 19 other fieldsHigh correlation
busotr is highly correlated with ocup1 and 20 other fieldsHigh correlation
cursnr is highly correlated with traant and 10 other fieldsHigh correlation
ocup1 is highly correlated with busotr and 16 other fieldsHigh correlation
traant is highly correlated with regest and 72 other fieldsHigh correlation
asala is highly correlated with busca and 9 other fieldsHigh correlation
nuevem is highly correlated with busca and 11 other fieldsHigh correlation
prore1 is highly correlated with traant and 4 other fieldsHigh correlation
repaire1 is highly correlated with traant and 4 other fieldsHigh correlation
ducon1 is highly correlated with busotr and 20 other fieldsHigh correlation
ocuplu1 is highly correlated with traant and 5 other fieldsHigh correlation
nac1 is highly correlated with traant and 6 other fieldsHigh correlation
disp is highly correlated with busca and 18 other fieldsHigh correlation
sitplu is highly correlated with traant and 4 other fieldsHigh correlation
tcontm is highly correlated with traant and 4 other fieldsHigh correlation
cse is highly correlated with busotr and 15 other fieldsHigh correlation
ayudfa is highly correlated with busca and 10 other fieldsHigh correlation
extpag is highly correlated with traant and 4 other fieldsHigh correlation
rzndis is highly correlated with busca and 8 other fieldsHigh correlation
trarem is highly correlated with busca and 29 other fieldsHigh correlation
parco1 is highly correlated with busotr and 21 other fieldsHigh correlation
ocupa is highly correlated with traant and 3 other fieldsHigh correlation
horplu is highly correlated with traant and 4 other fieldsHigh correlation
mun1 is highly correlated with traant and 5 other fieldsHigh correlation
hordes is highly correlated with traant and 5 other fieldsHigh correlation
aoi is highly correlated with busca and 26 other fieldsHigh correlation
tcontd is highly correlated with traant and 3 other fieldsHigh correlation
traplu is highly correlated with busotr and 24 other fieldsHigh correlation
actplu1 is highly correlated with traant and 5 other fieldsHigh correlation
eciv1 is highly correlated with cursnr and 9 other fieldsHigh correlation
ciclo is highly correlated with traant and 3 other fieldsHigh correlation
sidi2 is highly correlated with traant and 3 other fieldsHigh correlation
sidac2 is highly correlated with busca and 22 other fieldsHigh correlation
cursr is highly correlated with cursnr and 11 other fieldsHigh correlation
regna1 is highly correlated with traant and 5 other fieldsHigh correlation
vincul is highly correlated with traant and 5 other fieldsHigh correlation
sp is highly correlated with traant and 3 other fieldsHigh correlation
embus is highly correlated with busca and 7 other fieldsHigh correlation
itbu is highly correlated with busca and 7 other fieldsHigh correlation
extra is highly correlated with busotr and 21 other fieldsHigh correlation
sexo1 is highly correlated with traant and 3 other fieldsHigh correlation
ausent is highly correlated with busca and 22 other fieldsHigh correlation
nbusca is highly correlated with busca and 9 other fieldsHigh correlation
prona1 is highly correlated with traant and 4 other fieldsHigh correlation
mashor is highly correlated with busotr and 22 other fieldsHigh correlation
sidi3 is highly correlated with traant and 3 other fieldsHigh correlation
sidac1 is highly correlated with busca and 22 other fieldsHigh correlation
parco2 is highly correlated with traant and 4 other fieldsHigh correlation
extnpg is highly correlated with traant and 4 other fieldsHigh correlation
situ is highly correlated with busotr and 17 other fieldsHigh correlation
sidi1 is highly correlated with traant and 7 other fieldsHigh correlation
act1 is highly correlated with busotr and 15 other fieldsHigh correlation
ducon3 is highly correlated with traant and 4 other fieldsHigh correlation
nforma is highly correlated with cursnr and 8 other fieldsHigh correlation
rellb2 is highly correlated with regest and 72 other fieldsHigh correlation
desea is highly correlated with busca and 9 other fieldsHigh correlation
proest is highly correlated with busotr and 15 other fieldsHigh correlation
rzndish is highly correlated with traant and 4 other fieldsHigh correlation
fobact is highly correlated with busca and 9 other fieldsHigh correlation
rzdifh is highly correlated with traant and 4 other fieldsHigh correlation
empant is highly correlated with busca and 23 other fieldsHigh correlation
acta is highly correlated with traant and 3 other fieldsHigh correlation
ncursr is highly correlated with traant and 4 other fieldsHigh correlation
rellb1 is highly correlated with regest and 72 other fieldsHigh correlation
situa is highly correlated with traant and 3 other fieldsHigh correlation
dismas is highly correlated with traant and 7 other fieldsHigh correlation
exregna1 is highly correlated with traant and 5 other fieldsHigh correlation
ofemp is highly correlated with traant and 8 other fieldsHigh correlation
ducon2 is highly correlated with busotr and 15 other fieldsHigh correlation
ncurnr is highly correlated with cursnr and 4 other fieldsHigh correlation
nivel is highly correlated with cursnr and 11 other fieldsHigh correlation
rznotb is highly correlated with traant and 5 other fieldsHigh correlation
rellmili is highly correlated with regest and 72 other fieldsHigh correlation
anore1 has 283494 (90.6%) missing values Missing
edadest has 51531 (16.5%) missing values Missing
hcurnr has 296167 (94.7%) missing values Missing
dren has 213109 (68.1%) missing values Missing
dcom has 192199 (61.5%) missing values Missing
dtant has 202249 (64.7%) missing values Missing
aoi_num has 47779 (15.3%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
edad5 has 11925 (3.8%) zeros Zeros
ncony has 152453 (48.7%) zeros Zeros
npadre has 235176 (75.2%) zeros Zeros
nmadre has 216463 (69.2%) zeros Zeros
dren has 3730 (1.2%) zeros Zeros

Reproduction

Analysis started2021-12-08 11:36:07.406457
Analysis finished2021-12-08 11:44:55.534912
Duration8 minutes and 48.13 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct312749
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156374
Minimum0
Maximum312748
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:55.629089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15637.4
Q178187
median156374
Q3234561
95-th percentile297110.6
Maximum312748
Range312748
Interquartile range (IQR)156374

Descriptive statistics

Standard deviation90283.00401
Coefficient of variation (CV)0.5773530383
Kurtosis-1.2
Mean156374
Median Absolute Deviation (MAD)78187
Skewness1.103422589 × 10-15
Sum4.890581213 × 1010
Variance8151020812
MonotonicityStrictly increasing
2021-12-08T12:44:55.758808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
784511
 
< 0.1%
1173561
 
< 0.1%
1153091
 
< 0.1%
1214541
 
< 0.1%
1194071
 
< 0.1%
764001
 
< 0.1%
743531
 
< 0.1%
804981
 
< 0.1%
682121
 
< 0.1%
Other values (312739)312739
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
3127481
< 0.1%
3127471
< 0.1%
3127461
< 0.1%
3127451
< 0.1%
3127441
< 0.1%
3127431
< 0.1%
3127421
< 0.1%
3127411
< 0.1%
3127401
< 0.1%
3127391
< 0.1%

Unnamed: 0.1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct164764
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78411.79855
Minimum0
Maximum164763
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:55.897624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7818.4
Q139093
median78187
Q3117280
95-th percentile149125.6
Maximum164763
Range164763
Interquartile range (IQR)78187

Descriptive statistics

Standard deviation45529.07339
Coefficient of variation (CV)0.5806405953
Kurtosis-1.160518977
Mean78411.79855
Median Absolute Deviation (MAD)39094
Skewness0.02898759211
Sum2.452321159 × 1010
Variance2072896524
MonotonicityNot monotonic
2021-12-08T12:44:56.025902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20472
 
< 0.1%
774362
 
< 0.1%
1369872
 
< 0.1%
1390342
 
< 0.1%
1328892
 
< 0.1%
1349362
 
< 0.1%
794872
 
< 0.1%
815342
 
< 0.1%
753892
 
< 0.1%
712912
 
< 0.1%
Other values (164754)312729
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
12
< 0.1%
22
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
62
< 0.1%
72
< 0.1%
82
< 0.1%
92
< 0.1%
ValueCountFrequency (%)
1647631
< 0.1%
1647621
< 0.1%
1647611
< 0.1%
1647601
< 0.1%
1647591
< 0.1%
1647581
< 0.1%
1647571
< 0.1%
1647561
< 0.1%
1647551
< 0.1%
1647541
< 0.1%

ciclo
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
187
164764 
191
147985 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters938247
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row187
2nd row187
3rd row187
4th row187
5th row187

Common Values

ValueCountFrequency (%)
187164764
52.7%
191147985
47.3%

Length

2021-12-08T12:44:56.242341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:44:56.295466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
187164764
52.7%
191147985
47.3%

Most occurring characters

ValueCountFrequency (%)
1460734
49.1%
8164764
 
17.6%
7164764
 
17.6%
9147985
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number938247
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1460734
49.1%
8164764
 
17.6%
7164764
 
17.6%
9147985
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common938247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1460734
49.1%
8164764
 
17.6%
7164764
 
17.6%
9147985
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII938247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1460734
49.1%
8164764
 
17.6%
7164764
 
17.6%
9147985
 
15.8%

ccaa
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.496615497
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:56.807481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q312
95-th percentile16
Maximum52
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.110991619
Coefficient of variation (CV)0.7192265698
Kurtosis18.01813677
Mean8.496615497
Median Absolute Deviation (MAD)3
Skewness2.732666971
Sum2657308
Variance37.34421856
MonotonicityNot monotonic
2021-12-08T12:44:56.916012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
149864
15.9%
1237729
12.1%
931221
10.0%
729814
9.5%
1023909
 
7.6%
821008
 
6.7%
1318193
 
5.8%
1614739
 
4.7%
213659
 
4.4%
512525
 
4.0%
Other values (9)60088
19.2%
ValueCountFrequency (%)
149864
15.9%
213659
 
4.4%
39102
 
2.9%
47895
 
2.5%
512525
 
4.0%
67124
 
2.3%
729814
9.5%
821008
6.7%
931221
10.0%
1023909
7.6%
ValueCountFrequency (%)
521339
 
0.4%
511245
 
0.4%
175621
 
1.8%
1614739
 
4.7%
157351
 
2.4%
1410161
 
3.2%
1318193
5.8%
1237729
12.1%
1110250
 
3.3%
1023909
7.6%

prov
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.45135236
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:57.034612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q114
median28
Q338
95-th percentile48
Maximum52
Range51
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.0514719
Coefficient of variation (CV)0.5312194138
Kurtosis-1.173685447
Mean26.45135236
Median Absolute Deviation (MAD)13
Skewness-0.04946810382
Sum8272634
Variance197.4438625
MonotonicityNot monotonic
2021-12-08T12:44:57.169813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2818193
 
5.8%
816441
 
5.3%
3613524
 
4.3%
4113411
 
4.3%
1513196
 
4.2%
4612210
 
3.9%
3010161
 
3.2%
339102
 
2.9%
77895
 
2.5%
507477
 
2.4%
Other values (42)191139
61.1%
ValueCountFrequency (%)
13088
 
1.0%
23239
 
1.0%
35542
 
1.8%
43466
 
1.1%
52941
 
0.9%
65811
 
1.9%
77895
2.5%
816441
5.3%
93366
 
1.1%
104439
 
1.4%
ValueCountFrequency (%)
521339
 
0.4%
511245
 
0.4%
507477
2.4%
492620
 
0.8%
486019
1.9%
474577
 
1.5%
4612210
3.9%
455403
1.7%
443363
 
1.1%
435860
1.9%

nvivi
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66698
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31561.63339
Minimum1
Maximum66698
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:57.302976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3112
Q115627
median31421
Q347386
95-th percentile60251.6
Maximum66698
Range66697
Interquartile range (IQR)31759

Descriptive statistics

Standard deviation18419.36507
Coefficient of variation (CV)0.5835998676
Kurtosis-1.161118052
Mean31561.63339
Median Absolute Deviation (MAD)15883
Skewness0.04007255469
Sum9870869282
Variance339273009.6
MonotonicityNot monotonic
2021-12-08T12:44:57.436224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3591716
 
< 0.1%
3094214
 
< 0.1%
4094814
 
< 0.1%
3501914
 
< 0.1%
1801314
 
< 0.1%
1117114
 
< 0.1%
1540813
 
< 0.1%
928113
 
< 0.1%
4583013
 
< 0.1%
3700813
 
< 0.1%
Other values (66688)312611
> 99.9%
ValueCountFrequency (%)
16
< 0.1%
26
< 0.1%
38
< 0.1%
48
< 0.1%
52
 
< 0.1%
62
 
< 0.1%
72
 
< 0.1%
84
< 0.1%
94
< 0.1%
104
< 0.1%
ValueCountFrequency (%)
666984
< 0.1%
666975
< 0.1%
666962
 
< 0.1%
666952
 
< 0.1%
666945
< 0.1%
666932
 
< 0.1%
666925
< 0.1%
666913
< 0.1%
666902
 
< 0.1%
666896
< 0.1%

nivel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
264970 
2
47779 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1264970
84.7%
247779
 
15.3%

Length

2021-12-08T12:44:57.646682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:44:57.708657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1264970
84.7%
247779
 
15.3%

Most occurring characters

ValueCountFrequency (%)
1264970
84.7%
247779
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number312749
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1264970
84.7%
247779
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1264970
84.7%
247779
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1264970
84.7%
247779
 
15.3%

npers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.053259962
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:57.765235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.136283973
Coefficient of variation (CV)0.5534048262
Kurtosis1.466053656
Mean2.053259962
Median Absolute Deviation (MAD)1
Skewness1.13366087
Sum642155
Variance1.291141266
MonotonicityNot monotonic
2021-12-08T12:44:57.859527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1125405
40.1%
295750
30.6%
353887
17.2%
428608
 
9.1%
56593
 
2.1%
61734
 
0.6%
7512
 
0.2%
8176
 
0.1%
955
 
< 0.1%
1022
 
< 0.1%
Other values (3)7
 
< 0.1%
ValueCountFrequency (%)
1125405
40.1%
295750
30.6%
353887
17.2%
428608
 
9.1%
56593
 
2.1%
61734
 
0.6%
7512
 
0.2%
8176
 
0.1%
955
 
< 0.1%
1022
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
122
 
< 0.1%
114
 
< 0.1%
1022
 
< 0.1%
955
 
< 0.1%
8176
 
0.1%
7512
 
0.2%
61734
 
0.6%
56593
 
2.1%
428608
9.1%

edad5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.9334898
Minimum0
Maximum65
Zeros11925
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:57.951396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median45
Q360
95-th percentile65
Maximum65
Range65
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.84431319
Coefficient of variation (CV)0.5092239459
Kurtosis-1.061428969
Mean40.9334898
Median Absolute Deviation (MAD)20
Skewness-0.46639772
Sum12801908
Variance434.4853925
MonotonicityNot monotonic
2021-12-08T12:44:58.043361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6570583
22.6%
5025457
 
8.1%
4524913
 
8.0%
5524587
 
7.9%
4024110
 
7.7%
6022132
 
7.1%
1020576
 
6.6%
3518475
 
5.9%
515278
 
4.9%
2014816
 
4.7%
Other values (4)51822
16.6%
ValueCountFrequency (%)
011925
3.8%
515278
4.9%
1020576
6.6%
1612968
4.1%
2014816
4.7%
2512996
4.2%
3013933
4.5%
3518475
5.9%
4024110
7.7%
4524913
8.0%
ValueCountFrequency (%)
6570583
22.6%
6022132
 
7.1%
5524587
 
7.9%
5025457
 
8.1%
4524913
 
8.0%
4024110
 
7.7%
3518475
 
5.9%
3013933
 
4.5%
2512996
 
4.2%
2014816
 
4.7%

relpp1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.129906091
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:58.136517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.332150224
Coefficient of variation (CV)0.6254502157
Kurtosis4.951132347
Mean2.129906091
Median Absolute Deviation (MAD)1
Skewness1.885005237
Sum666126
Variance1.774624219
MonotonicityNot monotonic
2021-12-08T12:44:58.224812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1126333
40.4%
390576
29.0%
277629
24.8%
67743
 
2.5%
74865
 
1.6%
53174
 
1.0%
91184
 
0.4%
41048
 
0.3%
8197
 
0.1%
ValueCountFrequency (%)
1126333
40.4%
277629
24.8%
390576
29.0%
41048
 
0.3%
53174
 
1.0%
67743
 
2.5%
74865
 
1.6%
8197
 
0.1%
91184
 
0.4%
ValueCountFrequency (%)
91184
 
0.4%
8197
 
0.1%
74865
 
1.6%
67743
 
2.5%
53174
 
1.0%
41048
 
0.3%
390576
29.0%
277629
24.8%
1126333
40.4%

sexo1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
6
162187 
1
150562 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row6
3rd row6
4th row6
5th row1

Common Values

ValueCountFrequency (%)
6162187
51.9%
1150562
48.1%

Length

2021-12-08T12:44:58.420662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:44:58.483249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
6162187
51.9%
1150562
48.1%

Most occurring characters

ValueCountFrequency (%)
6162187
51.9%
1150562
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number312749
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6162187
51.9%
1150562
48.1%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6162187
51.9%
1150562
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6162187
51.9%
1150562
48.1%

ncony
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7992543541
Minimum0
Maximum9
Zeros152453
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:58.543175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8973915424
Coefficient of variation (CV)1.122785929
Kurtosis0.8981851539
Mean0.7992543541
Median Absolute Deviation (MAD)1
Skewness0.8704379681
Sum249966
Variance0.8053115803
MonotonicityNot monotonic
2021-12-08T12:44:58.632132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0152453
48.7%
278780
25.2%
177476
24.8%
32303
 
0.7%
41004
 
0.3%
5474
 
0.2%
6193
 
0.1%
752
 
< 0.1%
813
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
0152453
48.7%
177476
24.8%
278780
25.2%
32303
 
0.7%
41004
 
0.3%
5474
 
0.2%
6193
 
0.1%
752
 
< 0.1%
813
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
813
 
< 0.1%
752
 
< 0.1%
6193
 
0.1%
5474
 
0.2%
41004
 
0.3%
32303
 
0.7%
278780
25.2%
177476
24.8%
0152453
48.7%

npadre
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3507349344
Minimum0
Maximum9
Zeros235176
Zeros (%)75.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:58.720335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6890768932
Coefficient of variation (CV)1.964665694
Kurtosis6.886515697
Mean0.3507349344
Median Absolute Deviation (MAD)0
Skewness2.272969267
Sum109692
Variance0.4748269648
MonotonicityNot monotonic
2021-12-08T12:44:58.798588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0235176
75.2%
149473
 
15.8%
225770
 
8.2%
31229
 
0.4%
4685
 
0.2%
5285
 
0.1%
6102
 
< 0.1%
718
 
< 0.1%
810
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
0235176
75.2%
149473
 
15.8%
225770
 
8.2%
31229
 
0.4%
4685
 
0.2%
5285
 
0.1%
6102
 
< 0.1%
718
 
< 0.1%
810
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
810
 
< 0.1%
718
 
< 0.1%
6102
 
< 0.1%
5285
 
0.1%
4685
 
0.2%
31229
 
0.4%
225770
 
8.2%
149473
 
15.8%
0235176
75.2%

nmadre
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5193909493
Minimum0
Maximum9
Zeros216463
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:44:58.881848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8767923356
Coefficient of variation (CV)1.688116315
Kurtosis3.748973602
Mean0.5193909493
Median Absolute Deviation (MAD)0
Skewness1.763584513
Sum162439
Variance0.7687647997
MonotonicityNot monotonic
2021-12-08T12:44:58.959538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0216463
69.2%
251248
 
16.4%
139445
 
12.6%
33311
 
1.1%
41271
 
0.4%
5698
 
0.2%
6219
 
0.1%
776
 
< 0.1%
817
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
0216463
69.2%
139445
 
12.6%
251248
 
16.4%
33311
 
1.1%
41271
 
0.4%
5698
 
0.2%
6219
 
0.1%
776
 
< 0.1%
817
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
817
 
< 0.1%
776
 
< 0.1%
6219
 
0.1%
5698
 
0.2%
41271
 
0.4%
33311
 
1.1%
251248
 
16.4%
139445
 
12.6%
0216463
69.2%

rellmili
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
312749 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
312749
100.0%

Length

2021-12-08T12:44:59.142773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:44:59.204239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
312749
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator312749
100.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
312749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
312749
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312749
100.0%

eciv1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2
144096 
1
82291 
47779 
3
21787 
4
16796 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row
4th row4
5th row1

Common Values

ValueCountFrequency (%)
2144096
46.1%
182291
26.3%
47779
 
15.3%
321787
 
7.0%
416796
 
5.4%

Length

2021-12-08T12:44:59.372729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:44:59.438339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2144096
54.4%
182291
31.1%
321787
 
8.2%
416796
 
6.3%

Most occurring characters

ValueCountFrequency (%)
2144096
46.1%
182291
26.3%
47779
 
15.3%
321787
 
7.0%
416796
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number264970
84.7%
Space Separator47779
 
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2144096
54.4%
182291
31.1%
321787
 
8.2%
416796
 
6.3%
Space Separator
ValueCountFrequency (%)
47779
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2144096
46.1%
182291
26.3%
47779
 
15.3%
321787
 
7.0%
416796
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2144096
46.1%
182291
26.3%
47779
 
15.3%
321787
 
7.0%
416796
 
5.4%

prona1
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
27049 
28
 
16234
08
 
14007
41
 
12419
36
 
12189
Other values (48)
230851 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01

Common Values

ValueCountFrequency (%)
27049
 
8.6%
2816234
 
5.2%
0814007
 
4.5%
4112419
 
4.0%
3612189
 
3.9%
1511799
 
3.8%
4610055
 
3.2%
308729
 
2.8%
338506
 
2.7%
187493
 
2.4%
Other values (43)184269
58.9%

Length

2021-12-08T12:44:59.658803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2816234
 
5.7%
0814007
 
4.9%
4112419
 
4.3%
3612189
 
4.3%
1511799
 
4.1%
4610055
 
3.5%
308729
 
3.1%
338506
 
3.0%
187493
 
2.6%
237467
 
2.6%
Other values (42)176802
61.9%

Most occurring characters

ValueCountFrequency (%)
395035
15.2%
188019
14.1%
277218
12.3%
073775
11.8%
473488
11.7%
54098
8.6%
848977
7.8%
636545
 
5.8%
535412
 
5.7%
723033
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number571400
91.4%
Space Separator54098
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
395035
16.6%
188019
15.4%
277218
13.5%
073775
12.9%
473488
12.9%
848977
8.6%
636545
 
6.4%
535412
 
6.2%
723033
 
4.0%
919898
 
3.5%
Space Separator
ValueCountFrequency (%)
54098
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
395035
15.2%
188019
14.1%
277218
12.3%
073775
11.8%
473488
11.7%
54098
8.6%
848977
7.8%
636545
 
5.8%
535412
 
5.7%
723033
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
395035
15.2%
188019
14.1%
277218
12.3%
073775
11.8%
473488
11.7%
54098
8.6%
848977
7.8%
636545
 
5.8%
535412
 
5.7%
723033
 
3.7%

regna1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
285700 
350
 
9612
200
 
4909
115
 
3670
128
 
3018
Other values (8)
 
5840

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters938247
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
285700
91.4%
3509612
 
3.1%
2004909
 
1.6%
1153670
 
1.2%
1283018
 
1.0%
3102487
 
0.8%
1001484
 
0.5%
420737
 
0.2%
400556
 
0.2%
125327
 
0.1%
Other values (3)249
 
0.1%

Length

2021-12-08T12:44:59.853584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3509612
35.5%
2004909
18.1%
1153670
 
13.6%
1283018
 
11.2%
3102487
 
9.2%
1001484
 
5.5%
420737
 
2.7%
400556
 
2.1%
125327
 
1.2%
300147
 
0.5%
Other values (2)102
 
0.4%

Most occurring characters

ValueCountFrequency (%)
857100
91.4%
027152
 
2.9%
114736
 
1.6%
513631
 
1.5%
312246
 
1.3%
28991
 
1.0%
83018
 
0.3%
41373
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator857100
91.4%
Decimal Number81147
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027152
33.5%
114736
18.2%
513631
16.8%
312246
15.1%
28991
 
11.1%
83018
 
3.7%
41373
 
1.7%
Space Separator
ValueCountFrequency (%)
857100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common938247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
857100
91.4%
027152
 
2.9%
114736
 
1.6%
513631
 
1.5%
312246
 
1.3%
28991
 
1.0%
83018
 
0.3%
41373
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII938247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
857100
91.4%
027152
 
2.9%
114736
 
1.6%
513631
 
1.5%
312246
 
1.3%
28991
 
1.0%
83018
 
0.3%
41373
 
0.1%

nac1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
287825 
3
 
17772
2
 
7152

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1287825
92.0%
317772
 
5.7%
27152
 
2.3%

Length

2021-12-08T12:45:00.051406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:00.117017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1287825
92.0%
317772
 
5.7%
27152
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1287825
92.0%
317772
 
5.7%
27152
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number312749
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1287825
92.0%
317772
 
5.7%
27152
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1287825
92.0%
317772
 
5.7%
27152
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1287825
92.0%
317772
 
5.7%
27152
 
2.3%

exregna1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
287825 
350
 
8423
200
 
5058
128
 
3480
115
 
2908
Other values (9)
 
5055

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters938247
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
287825
92.0%
3508423
 
2.7%
2005058
 
1.6%
1283480
 
1.1%
1152908
 
0.9%
3102129
 
0.7%
1001076
 
0.3%
420660
 
0.2%
400562
 
0.2%
125369
 
0.1%
Other values (4)259
 
0.1%

Length

2021-12-08T12:45:00.305152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3508423
33.8%
2005058
20.3%
1283480
14.0%
1152908
 
11.7%
3102129
 
8.5%
1001076
 
4.3%
420660
 
2.6%
400562
 
2.3%
125369
 
1.5%
300165
 
0.7%
Other values (3)94
 
0.4%

Most occurring characters

ValueCountFrequency (%)
863475
92.0%
025039
 
2.7%
112921
 
1.4%
511727
 
1.2%
310717
 
1.1%
29567
 
1.0%
83480
 
0.4%
41273
 
0.1%
948
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator863475
92.0%
Decimal Number74772
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025039
33.5%
112921
17.3%
511727
15.7%
310717
14.3%
29567
 
12.8%
83480
 
4.7%
41273
 
1.7%
948
 
0.1%
Space Separator
ValueCountFrequency (%)
863475
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common938247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
863475
92.0%
025039
 
2.7%
112921
 
1.4%
511727
 
1.2%
310717
 
1.1%
29567
 
1.0%
83480
 
0.4%
41273
 
0.1%
948
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII938247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
863475
92.0%
025039
 
2.7%
112921
 
1.4%
511727
 
1.2%
310717
 
1.1%
29567
 
1.0%
83480
 
0.4%
41273
 
0.1%
948
 
< 0.1%

anore1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct95
Distinct (%)0.3%
Missing283494
Missing (%)90.6%
Infinite0
Infinite (%)0.0%
Mean16.32103914
Minimum0
Maximum94
Zeros752
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:45:00.417726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median14
Q319
95-th percentile46
Maximum94
Range94
Interquartile range (IQR)11

Descriptive statistics

Standard deviation13.3732012
Coefficient of variation (CV)0.8193841759
Kurtosis3.699301634
Mean16.32103914
Median Absolute Deviation (MAD)6
Skewness1.695982964
Sum477472
Variance178.8425104
MonotonicityNot monotonic
2021-12-08T12:45:00.550917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121525
 
0.5%
131457
 
0.5%
151439
 
0.5%
171436
 
0.5%
161429
 
0.5%
141422
 
0.5%
181408
 
0.5%
191376
 
0.4%
11336
 
0.4%
111301
 
0.4%
Other values (85)15126
 
4.8%
(Missing)283494
90.6%
ValueCountFrequency (%)
0752
0.2%
11336
0.4%
21164
0.4%
31018
0.3%
4830
0.3%
5692
0.2%
6616
0.2%
7694
0.2%
8657
0.2%
9727
0.2%
ValueCountFrequency (%)
941
 
< 0.1%
931
 
< 0.1%
923
 
< 0.1%
914
 
< 0.1%
906
< 0.1%
895
< 0.1%
887
< 0.1%
8712
< 0.1%
865
< 0.1%
851
 
< 0.1%

nforma
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
S1
79097 
SU
77194 
47779 
P2
36236 
SG
33927 
Other values (3)
38516 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSG
2nd rowSP
3rd row
4th rowSU
5th rowS1

Common Values

ValueCountFrequency (%)
S179097
25.3%
SU77194
24.7%
47779
15.3%
P236236
11.6%
SG33927
10.8%
SP20828
 
6.7%
P113936
 
4.5%
AN3752
 
1.2%

Length

2021-12-08T12:45:00.791174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:00.865310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
s179097
29.9%
su77194
29.1%
p236236
13.7%
sg33927
12.8%
sp20828
 
7.9%
p113936
 
5.3%
an3752
 
1.4%

Most occurring characters

ValueCountFrequency (%)
S211046
33.7%
95558
15.3%
193033
14.9%
U77194
 
12.3%
P71000
 
11.4%
236236
 
5.8%
G33927
 
5.4%
A3752
 
0.6%
N3752
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter400671
64.1%
Decimal Number129269
 
20.7%
Space Separator95558
 
15.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S211046
52.7%
U77194
 
19.3%
P71000
 
17.7%
G33927
 
8.5%
A3752
 
0.9%
N3752
 
0.9%
Decimal Number
ValueCountFrequency (%)
193033
72.0%
236236
 
28.0%
Space Separator
ValueCountFrequency (%)
95558
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin400671
64.1%
Common224827
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
S211046
52.7%
U77194
 
19.3%
P71000
 
17.7%
G33927
 
8.5%
A3752
 
0.9%
N3752
 
0.9%
Common
ValueCountFrequency (%)
95558
42.5%
193033
41.4%
236236
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S211046
33.7%
95558
15.3%
193033
14.9%
U77194
 
12.3%
P71000
 
11.4%
236236
 
5.8%
G33927
 
5.4%
A3752
 
0.6%
N3752
 
0.6%

rellb1
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
312749 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
312749
100.0%

Length

2021-12-08T12:45:01.060098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:01.123077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
625498
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator625498
100.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
625498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
625498
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
625498
100.0%

edadest
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct74
Distinct (%)< 0.1%
Missing51531
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean18.95736128
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:45:01.201623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q114
median18
Q322
95-th percentile32
Maximum85
Range78
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.998641963
Coefficient of variation (CV)0.3691780654
Kurtosis6.809648264
Mean18.95736128
Median Absolute Deviation (MAD)4
Skewness2.11134294
Sum4952004
Variance48.98098933
MonotonicityNot monotonic
2021-12-08T12:45:01.326537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1452051
16.6%
1828676
 
9.2%
1621265
 
6.8%
1713449
 
4.3%
1212462
 
4.0%
1512095
 
3.9%
2312037
 
3.8%
1911854
 
3.8%
2011542
 
3.7%
2211178
 
3.6%
Other values (64)74609
23.9%
(Missing)51531
16.5%
ValueCountFrequency (%)
7412
 
0.1%
8919
 
0.3%
91197
 
0.4%
105122
 
1.6%
112431
 
0.8%
1212462
 
4.0%
137703
 
2.5%
1452051
16.6%
1512095
 
3.9%
1621265
6.8%
ValueCountFrequency (%)
851
 
< 0.1%
831
 
< 0.1%
812
 
< 0.1%
771
 
< 0.1%
763
< 0.1%
752
 
< 0.1%
742
 
< 0.1%
734
< 0.1%
726
< 0.1%
715
< 0.1%

cursr
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
3
239373 
47779 
1
24934 
2
 
663

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3239373
76.5%
47779
 
15.3%
124934
 
8.0%
2663
 
0.2%

Length

2021-12-08T12:45:01.545974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:01.613611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3239373
90.3%
124934
 
9.4%
2663
 
0.3%

Most occurring characters

ValueCountFrequency (%)
3239373
76.5%
47779
 
15.3%
124934
 
8.0%
2663
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number264970
84.7%
Space Separator47779
 
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3239373
90.3%
124934
 
9.4%
2663
 
0.3%
Space Separator
ValueCountFrequency (%)
47779
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3239373
76.5%
47779
 
15.3%
124934
 
8.0%
2663
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3239373
76.5%
47779
 
15.3%
124934
 
8.0%
2663
 
0.2%

ncursr
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
287152 
SU
 
13696
SG
 
6601
SP
 
3962
S1
 
1270

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
287152
91.8%
SU13696
 
4.4%
SG6601
 
2.1%
SP3962
 
1.3%
S11270
 
0.4%
PR68
 
< 0.1%

Length

2021-12-08T12:45:01.794757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:01.867399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
su13696
53.5%
sg6601
25.8%
sp3962
 
15.5%
s11270
 
5.0%
pr68
 
0.3%

Most occurring characters

ValueCountFrequency (%)
574304
91.8%
S25529
 
4.1%
U13696
 
2.2%
G6601
 
1.1%
P4030
 
0.6%
11270
 
0.2%
R68
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator574304
91.8%
Uppercase Letter49924
 
8.0%
Decimal Number1270
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S25529
51.1%
U13696
27.4%
G6601
 
13.2%
P4030
 
8.1%
R68
 
0.1%
Space Separator
ValueCountFrequency (%)
574304
100.0%
Decimal Number
ValueCountFrequency (%)
11270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common575574
92.0%
Latin49924
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S25529
51.1%
U13696
27.4%
G6601
 
13.2%
P4030
 
8.1%
R68
 
0.1%
Common
ValueCountFrequency (%)
574304
99.8%
11270
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
574304
91.8%
S25529
 
4.1%
U13696
 
2.2%
G6601
 
1.1%
P4030
 
0.6%
11270
 
0.2%
R68
 
< 0.1%

cursnr
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
3
248388 
47779 
1
 
16557
2
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3248388
79.4%
47779
 
15.3%
116557
 
5.3%
225
 
< 0.1%

Length

2021-12-08T12:45:02.048536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:02.113172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3248388
93.7%
116557
 
6.2%
225
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
3248388
79.4%
47779
 
15.3%
116557
 
5.3%
225
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number264970
84.7%
Space Separator47779
 
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3248388
93.7%
116557
 
6.2%
225
 
< 0.1%
Space Separator
ValueCountFrequency (%)
47779
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3248388
79.4%
47779
 
15.3%
116557
 
5.3%
225
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3248388
79.4%
47779
 
15.3%
116557
 
5.3%
225
 
< 0.1%

ncurnr
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
296167 
PE
 
15141
EM
 
808
ED
 
633

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
296167
94.7%
PE15141
 
4.8%
EM808
 
0.3%
ED633
 
0.2%

Length

2021-12-08T12:45:02.292059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:02.356362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
pe15141
91.3%
em808
 
4.9%
ed633
 
3.8%

Most occurring characters

ValueCountFrequency (%)
592334
94.7%
E16582
 
2.7%
P15141
 
2.4%
M808
 
0.1%
D633
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator592334
94.7%
Uppercase Letter33164
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E16582
50.0%
P15141
45.7%
M808
 
2.4%
D633
 
1.9%
Space Separator
ValueCountFrequency (%)
592334
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common592334
94.7%
Latin33164
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E16582
50.0%
P15141
45.7%
M808
 
2.4%
D633
 
1.9%
Common
ValueCountFrequency (%)
592334
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
592334
94.7%
E16582
 
2.7%
P15141
 
2.4%
M808
 
0.1%
D633
 
0.1%

hcurnr
Real number (ℝ≥0)

MISSING

Distinct123
Distinct (%)0.7%
Missing296167
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean319.4995779
Minimum0
Maximum999
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:45:02.448176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112
median30
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)987

Descriptive statistics

Standard deviation444.249974
Coefficient of variation (CV)1.39045559
Kurtosis-1.231220921
Mean319.4995779
Median Absolute Deviation (MAD)25
Skewness0.8649831997
Sum5297942
Variance197358.0394
MonotonicityNot monotonic
2021-12-08T12:45:02.577353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9994945
 
1.6%
201347
 
0.4%
81079
 
0.3%
10786
 
0.3%
12778
 
0.2%
40694
 
0.2%
16602
 
0.2%
4578
 
0.2%
30455
 
0.1%
6378
 
0.1%
Other values (113)4940
 
1.6%
(Missing)296167
94.7%
ValueCountFrequency (%)
016
 
< 0.1%
1130
 
< 0.1%
2362
 
0.1%
3279
 
0.1%
4578
0.2%
5329
 
0.1%
6378
 
0.1%
789
 
< 0.1%
81079
0.3%
951
 
< 0.1%
ValueCountFrequency (%)
9994945
1.6%
4005
 
< 0.1%
3921
 
< 0.1%
3351
 
< 0.1%
3203
 
< 0.1%
3081
 
< 0.1%
30019
 
< 0.1%
2851
 
< 0.1%
28017
 
< 0.1%
2642
 
< 0.1%

rellb2
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
312749 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
312749
100.0%

Length

2021-12-08T12:45:02.778167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:02.840761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
625498
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator625498
100.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
625498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
625498
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
625498
100.0%

trarem
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
6
162430 
1
102540 
47779 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row6
3rd row
4th row1
5th row6

Common Values

ValueCountFrequency (%)
6162430
51.9%
1102540
32.8%
47779
 
15.3%

Length

2021-12-08T12:45:02.992626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:03.057217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
6162430
61.3%
1102540
38.7%

Most occurring characters

ValueCountFrequency (%)
6162430
51.9%
1102540
32.8%
47779
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number264970
84.7%
Space Separator47779
 
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6162430
61.3%
1102540
38.7%
Space Separator
ValueCountFrequency (%)
47779
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6162430
51.9%
1102540
32.8%
47779
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6162430
51.9%
1102540
32.8%
47779
 
15.3%

ayudfa
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
6
161894 
150319 
1
 
536

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row6
3rd row
4th row
5th row6

Common Values

ValueCountFrequency (%)
6161894
51.8%
150319
48.1%
1536
 
0.2%

Length

2021-12-08T12:45:03.237757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:03.300374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
6161894
99.7%
1536
 
0.3%

Most occurring characters

ValueCountFrequency (%)
6161894
51.8%
150319
48.1%
1536
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number162430
51.9%
Space Separator150319
48.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6161894
99.7%
1536
 
0.3%
Space Separator
ValueCountFrequency (%)
150319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6161894
51.8%
150319
48.1%
1536
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6161894
51.8%
150319
48.1%
1536
 
0.2%

ausent
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
150319 
6
143126 
1
19304 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row6
3rd row
4th row
5th row6

Common Values

ValueCountFrequency (%)
150319
48.1%
6143126
45.8%
119304
 
6.2%

Length

2021-12-08T12:45:03.475548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:04.034067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
6143126
88.1%
119304
 
11.9%

Most occurring characters

ValueCountFrequency (%)
150319
48.1%
6143126
45.8%
119304
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number162430
51.9%
Space Separator150319
48.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6143126
88.1%
119304
 
11.9%
Space Separator
ValueCountFrequency (%)
150319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
150319
48.1%
6143126
45.8%
119304
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
150319
48.1%
6143126
45.8%
119304
 
6.2%

rznotb
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
293549 
11
 
5887
10
 
4869
04
 
4384
01
 
2242
Other values (12)
 
1818

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
293549
93.9%
115887
 
1.9%
104869
 
1.6%
044384
 
1.4%
012242
 
0.7%
15543
 
0.2%
02496
 
0.2%
08272
 
0.1%
03196
 
0.1%
14119
 
< 0.1%
Other values (7)192
 
0.1%

Length

2021-12-08T12:45:04.226808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
115887
30.7%
104869
25.4%
044384
22.8%
012242
 
11.7%
15543
 
2.8%
02496
 
2.6%
08272
 
1.4%
03196
 
1.0%
14119
 
0.6%
0588
 
0.5%
Other values (6)104
 
0.5%

Most occurring characters

ValueCountFrequency (%)
587098
93.9%
119557
 
3.1%
012654
 
2.0%
44503
 
0.7%
5631
 
0.1%
2499
 
0.1%
8272
 
< 0.1%
3203
 
< 0.1%
758
 
< 0.1%
917
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator587098
93.9%
Decimal Number38400
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
119557
50.9%
012654
33.0%
44503
 
11.7%
5631
 
1.6%
2499
 
1.3%
8272
 
0.7%
3203
 
0.5%
758
 
0.2%
917
 
< 0.1%
66
 
< 0.1%
Space Separator
ValueCountFrequency (%)
587098
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
587098
93.9%
119557
 
3.1%
012654
 
2.0%
44503
 
0.7%
5631
 
0.1%
2499
 
0.1%
8272
 
< 0.1%
3203
 
< 0.1%
758
 
< 0.1%
917
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
587098
93.9%
119557
 
3.1%
012654
 
2.0%
44503
 
0.7%
5631
 
0.1%
2499
 
0.1%
8272
 
< 0.1%
3203
 
< 0.1%
758
 
< 0.1%
917
 
< 0.1%

vincul
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
293549 
05
 
7668
01
 
6626
03
 
2654
06
 
1389
Other values (4)
 
863

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
293549
93.9%
057668
 
2.5%
016626
 
2.1%
032654
 
0.8%
061389
 
0.4%
02496
 
0.2%
08330
 
0.1%
0430
 
< 0.1%
077
 
< 0.1%

Length

2021-12-08T12:45:04.434613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:04.505879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
057668
39.9%
016626
34.5%
032654
 
13.8%
061389
 
7.2%
02496
 
2.6%
08330
 
1.7%
0430
 
0.2%
077
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
587098
93.9%
019200
 
3.1%
57668
 
1.2%
16626
 
1.1%
32654
 
0.4%
61389
 
0.2%
2496
 
0.1%
8330
 
0.1%
430
 
< 0.1%
77
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator587098
93.9%
Decimal Number38400
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019200
50.0%
57668
 
20.0%
16626
 
17.3%
32654
 
6.9%
61389
 
3.6%
2496
 
1.3%
8330
 
0.9%
430
 
0.1%
77
 
< 0.1%
Space Separator
ValueCountFrequency (%)
587098
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
587098
93.9%
019200
 
3.1%
57668
 
1.2%
16626
 
1.1%
32654
 
0.4%
61389
 
0.2%
2496
 
0.1%
8330
 
0.1%
430
 
< 0.1%
77
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
587098
93.9%
019200
 
3.1%
57668
 
1.2%
16626
 
1.1%
32654
 
0.4%
61389
 
0.2%
2496
 
0.1%
8330
 
0.1%
430
 
< 0.1%
77
 
< 0.1%

nuevem
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
202645 
3
107675 
1
 
2135
2
 
294

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row3
3rd row
4th row
5th row3

Common Values

ValueCountFrequency (%)
202645
64.8%
3107675
34.4%
12135
 
0.7%
2294
 
0.1%

Length

2021-12-08T12:45:04.720840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:04.786326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3107675
97.8%
12135
 
1.9%
2294
 
0.3%

Most occurring characters

ValueCountFrequency (%)
202645
64.8%
3107675
34.4%
12135
 
0.7%
2294
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator202645
64.8%
Decimal Number110104
35.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3107675
97.8%
12135
 
1.9%
2294
 
0.3%
Space Separator
ValueCountFrequency (%)
202645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
202645
64.8%
3107675
34.4%
12135
 
0.7%
2294
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
202645
64.8%
3107675
34.4%
12135
 
0.7%
2294
 
0.1%

ocup1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
5
25863 
2
23086 
9
 
13948
7
 
13602
Other values (6)
44051 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row
3rd row
4th row5
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
525863
 
8.3%
223086
 
7.4%
913948
 
4.5%
713602
 
4.3%
312765
 
4.1%
412561
 
4.0%
89560
 
3.1%
14878
 
1.6%
63542
 
1.1%

Length

2021-12-08T12:45:04.983091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
525863
21.5%
223086
19.2%
913948
11.6%
713602
11.3%
312765
10.6%
412561
10.4%
89560
 
7.9%
14878
 
4.0%
63542
 
2.9%
0745
 
0.6%

Most occurring characters

ValueCountFrequency (%)
192199
61.5%
525863
 
8.3%
223086
 
7.4%
913948
 
4.5%
713602
 
4.3%
312765
 
4.1%
412561
 
4.0%
89560
 
3.1%
14878
 
1.6%
63542
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator192199
61.5%
Decimal Number120550
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
525863
21.5%
223086
19.2%
913948
11.6%
713602
11.3%
312765
10.6%
412561
10.4%
89560
 
7.9%
14878
 
4.0%
63542
 
2.9%
0745
 
0.6%
Space Separator
ValueCountFrequency (%)
192199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192199
61.5%
525863
 
8.3%
223086
 
7.4%
913948
 
4.5%
713602
 
4.3%
312765
 
4.1%
412561
 
4.0%
89560
 
3.1%
14878
 
1.6%
63542
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192199
61.5%
525863
 
8.3%
223086
 
7.4%
913948
 
4.5%
713602
 
4.3%
312765
 
4.1%
412561
 
4.0%
89560
 
3.1%
14878
 
1.6%
63542
 
1.1%

act1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
8
30710 
5
27047 
7
 
15021
6
 
8809
Other values (6)
38963 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row
3rd row
4th row5
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
830710
 
9.8%
527047
 
8.6%
715021
 
4.8%
68809
 
2.8%
98103
 
2.6%
47484
 
2.4%
26689
 
2.1%
15857
 
1.9%
05613
 
1.8%

Length

2021-12-08T12:45:05.201508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
830710
25.5%
527047
22.4%
715021
12.5%
68809
 
7.3%
98103
 
6.7%
47484
 
6.2%
26689
 
5.5%
15857
 
4.9%
05613
 
4.7%
35217
 
4.3%

Most occurring characters

ValueCountFrequency (%)
192199
61.5%
830710
 
9.8%
527047
 
8.6%
715021
 
4.8%
68809
 
2.8%
98103
 
2.6%
47484
 
2.4%
26689
 
2.1%
15857
 
1.9%
05613
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator192199
61.5%
Decimal Number120550
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
830710
25.5%
527047
22.4%
715021
12.5%
68809
 
7.3%
98103
 
6.7%
47484
 
6.2%
26689
 
5.5%
15857
 
4.9%
05613
 
4.7%
35217
 
4.3%
Space Separator
ValueCountFrequency (%)
192199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192199
61.5%
830710
 
9.8%
527047
 
8.6%
715021
 
4.8%
68809
 
2.8%
98103
 
2.6%
47484
 
2.4%
26689
 
2.1%
15857
 
1.9%
05613
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192199
61.5%
830710
 
9.8%
527047
 
8.6%
715021
 
4.8%
68809
 
2.8%
98103
 
2.6%
47484
 
2.4%
26689
 
2.1%
15857
 
1.9%
05613
 
1.8%

situ
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
08
75940 
07
23700 
03
 
13835
01
 
6399
Other values (3)
 
676

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row08
2nd row
3rd row
4th row08
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
0875940
 
24.3%
0723700
 
7.6%
0313835
 
4.4%
016399
 
2.0%
06445
 
0.1%
05198
 
0.1%
0933
 
< 0.1%

Length

2021-12-08T12:45:05.416012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:05.489300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0875940
63.0%
0723700
 
19.7%
0313835
 
11.5%
016399
 
5.3%
06445
 
0.4%
05198
 
0.2%
0933
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
384398
61.5%
0120550
 
19.3%
875940
 
12.1%
723700
 
3.8%
313835
 
2.2%
16399
 
1.0%
6445
 
0.1%
5198
 
< 0.1%
933
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator384398
61.5%
Decimal Number241100
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0120550
50.0%
875940
31.5%
723700
 
9.8%
313835
 
5.7%
16399
 
2.7%
6445
 
0.2%
5198
 
0.1%
933
 
< 0.1%
Space Separator
ValueCountFrequency (%)
384398
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
384398
61.5%
0120550
 
19.3%
875940
 
12.1%
723700
 
3.8%
313835
 
2.2%
16399
 
1.0%
6445
 
0.1%
5198
 
< 0.1%
933
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
384398
61.5%
0120550
 
19.3%
875940
 
12.1%
723700
 
3.8%
313835
 
2.2%
16399
 
1.0%
6445
 
0.1%
5198
 
< 0.1%
933
 
< 0.1%

sp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
289049 
3
 
14194
4
 
4494
1
 
3747
5
 
943
Other values (3)
 
322

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
289049
92.4%
314194
 
4.5%
44494
 
1.4%
13747
 
1.2%
5943
 
0.3%
2238
 
0.1%
663
 
< 0.1%
021
 
< 0.1%

Length

2021-12-08T12:45:05.687413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:05.755682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
314194
59.9%
44494
 
19.0%
13747
 
15.8%
5943
 
4.0%
2238
 
1.0%
663
 
0.3%
021
 
0.1%

Most occurring characters

ValueCountFrequency (%)
289049
92.4%
314194
 
4.5%
44494
 
1.4%
13747
 
1.2%
5943
 
0.3%
2238
 
0.1%
663
 
< 0.1%
021
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator289049
92.4%
Decimal Number23700
 
7.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
314194
59.9%
44494
 
19.0%
13747
 
15.8%
5943
 
4.0%
2238
 
1.0%
663
 
0.3%
021
 
0.1%
Space Separator
ValueCountFrequency (%)
289049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
289049
92.4%
314194
 
4.5%
44494
 
1.4%
13747
 
1.2%
5943
 
0.3%
2238
 
0.1%
663
 
< 0.1%
021
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
289049
92.4%
314194
 
4.5%
44494
 
1.4%
13747
 
1.2%
5943
 
0.3%
2238
 
0.1%
663
 
< 0.1%
021
 
< 0.1%

ducon1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
213109 
1
75899 
6
23741 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row
4th row1
5th row

Common Values

ValueCountFrequency (%)
213109
68.1%
175899
 
24.3%
623741
 
7.6%

Length

2021-12-08T12:45:05.955523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:06.022103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
175899
76.2%
623741
 
23.8%

Most occurring characters

ValueCountFrequency (%)
213109
68.1%
175899
 
24.3%
623741
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator213109
68.1%
Decimal Number99640
31.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
175899
76.2%
623741
 
23.8%
Space Separator
ValueCountFrequency (%)
213109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
213109
68.1%
175899
 
24.3%
623741
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
213109
68.1%
175899
 
24.3%
623741
 
7.6%

ducon2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
236850 
1
73778 
6
 
2121

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row
4th row1
5th row

Common Values

ValueCountFrequency (%)
236850
75.7%
173778
 
23.6%
62121
 
0.7%

Length

2021-12-08T12:45:06.193623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:06.260225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
173778
97.2%
62121
 
2.8%

Most occurring characters

ValueCountFrequency (%)
236850
75.7%
173778
 
23.6%
62121
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator236850
75.7%
Decimal Number75899
 
24.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
173778
97.2%
62121
 
2.8%
Space Separator
ValueCountFrequency (%)
236850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
236850
75.7%
173778
 
23.6%
62121
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
236850
75.7%
173778
 
23.6%
62121
 
0.7%

ducon3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
289008 
06
 
9005
01
 
4702
05
 
3959
00
 
1751
Other values (5)
 
4324

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
289008
92.4%
069005
 
2.9%
014702
 
1.5%
053959
 
1.3%
001751
 
0.6%
031578
 
0.5%
021025
 
0.3%
08960
 
0.3%
07549
 
0.2%
04212
 
0.1%

Length

2021-12-08T12:45:06.435853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:06.512341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
069005
37.9%
014702
19.8%
053959
16.7%
001751
 
7.4%
031578
 
6.6%
021025
 
4.3%
08960
 
4.0%
07549
 
2.3%
04212
 
0.9%

Most occurring characters

ValueCountFrequency (%)
578016
92.4%
025492
 
4.1%
69005
 
1.4%
14702
 
0.8%
53959
 
0.6%
31578
 
0.3%
21025
 
0.2%
8960
 
0.2%
7549
 
0.1%
4212
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator578016
92.4%
Decimal Number47482
 
7.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025492
53.7%
69005
 
19.0%
14702
 
9.9%
53959
 
8.3%
31578
 
3.3%
21025
 
2.2%
8960
 
2.0%
7549
 
1.2%
4212
 
0.4%
Space Separator
ValueCountFrequency (%)
578016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
578016
92.4%
025492
 
4.1%
69005
 
1.4%
14702
 
0.8%
53959
 
0.6%
31578
 
0.3%
21025
 
0.2%
8960
 
0.2%
7549
 
0.1%
4212
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
578016
92.4%
025492
 
4.1%
69005
 
1.4%
14702
 
0.8%
53959
 
0.6%
31578
 
0.3%
21025
 
0.2%
8960
 
0.2%
7549
 
0.1%
4212
 
< 0.1%

tcontm
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
291172 
00
 
11247
06
 
2703
12
 
2360
03
 
1086
Other values (48)
 
4181

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
291172
93.1%
0011247
 
3.6%
062703
 
0.9%
122360
 
0.8%
031086
 
0.3%
09849
 
0.3%
01406
 
0.1%
48362
 
0.1%
10325
 
0.1%
04315
 
0.1%
Other values (43)1924
 
0.6%

Length

2021-12-08T12:45:06.764733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0011247
52.1%
062703
 
12.5%
122360
 
10.9%
031086
 
5.0%
09849
 
3.9%
01406
 
1.9%
48362
 
1.7%
10325
 
1.5%
04315
 
1.5%
02296
 
1.4%
Other values (42)1628
 
7.5%

Most occurring characters

ValueCountFrequency (%)
582344
93.1%
029115
 
4.7%
13404
 
0.5%
63252
 
0.5%
22967
 
0.5%
31343
 
0.2%
91088
 
0.2%
4971
 
0.2%
8671
 
0.1%
5206
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator582344
93.1%
Decimal Number43154
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029115
67.5%
13404
 
7.9%
63252
 
7.5%
22967
 
6.9%
31343
 
3.1%
91088
 
2.5%
4971
 
2.3%
8671
 
1.6%
5206
 
0.5%
7137
 
0.3%
Space Separator
ValueCountFrequency (%)
582344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
582344
93.1%
029115
 
4.7%
13404
 
0.5%
63252
 
0.5%
22967
 
0.5%
31343
 
0.2%
91088
 
0.2%
4971
 
0.2%
8671
 
0.1%
5206
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
582344
93.1%
029115
 
4.7%
13404
 
0.5%
63252
 
0.5%
22967
 
0.5%
31343
 
0.2%
91088
 
0.2%
4971
 
0.2%
8671
 
0.1%
5206
 
< 0.1%

tcontd
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
310585 
99
 
1109
00
 
311
01
 
142
15
 
133
Other values (22)
 
469

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
310585
99.3%
991109
 
0.4%
00311
 
0.1%
01142
 
< 0.1%
15133
 
< 0.1%
02118
 
< 0.1%
0394
 
< 0.1%
0777
 
< 0.1%
0529
 
< 0.1%
0425
 
< 0.1%
Other values (17)126
 
< 0.1%

Length

2021-12-08T12:45:06.974192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
991109
51.2%
00311
 
14.4%
01142
 
6.6%
15133
 
6.1%
02118
 
5.5%
0394
 
4.3%
0777
 
3.6%
0529
 
1.3%
0425
 
1.2%
2119
 
0.9%
Other values (16)107
 
4.9%

Most occurring characters

ValueCountFrequency (%)
621170
99.3%
92220
 
0.4%
01178
 
0.2%
1344
 
0.1%
2170
 
< 0.1%
5164
 
< 0.1%
3101
 
< 0.1%
784
 
< 0.1%
440
 
< 0.1%
619
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator621170
99.3%
Decimal Number4328
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
92220
51.3%
01178
27.2%
1344
 
7.9%
2170
 
3.9%
5164
 
3.8%
3101
 
2.3%
784
 
1.9%
440
 
0.9%
619
 
0.4%
88
 
0.2%
Space Separator
ValueCountFrequency (%)
621170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
621170
99.3%
92220
 
0.4%
01178
 
0.2%
1344
 
0.1%
2170
 
< 0.1%
5164
 
< 0.1%
3101
 
< 0.1%
784
 
< 0.1%
440
 
< 0.1%
619
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
621170
99.3%
92220
 
0.4%
01178
 
0.2%
1344
 
0.1%
2170
 
< 0.1%
5164
 
< 0.1%
3101
 
< 0.1%
784
 
< 0.1%
440
 
< 0.1%
619
 
< 0.1%

dren
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct598
Distinct (%)0.6%
Missing213109
Missing (%)68.1%
Infinite0
Infinite (%)0.0%
Mean118.6873244
Minimum0
Maximum720
Zeros3730
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:45:07.085276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median68
Q3192
95-th percentile380
Maximum720
Range720
Interquartile range (IQR)182

Descriptive statistics

Standard deviation127.4069775
Coefficient of variation (CV)1.073467434
Kurtosis0.3640839249
Mean118.6873244
Median Absolute Deviation (MAD)64
Skewness1.093873082
Sum11826005
Variance16232.53792
MonotonicityNot monotonic
2021-12-08T12:45:07.217298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03730
 
1.2%
13220
 
1.0%
32898
 
0.9%
42550
 
0.8%
22432
 
0.8%
72244
 
0.7%
51963
 
0.6%
81938
 
0.6%
61809
 
0.6%
91696
 
0.5%
Other values (588)75160
 
24.0%
(Missing)213109
68.1%
ValueCountFrequency (%)
03730
1.2%
13220
1.0%
22432
0.8%
32898
0.9%
42550
0.8%
51963
0.6%
61809
0.6%
72244
0.7%
81938
0.6%
91696
0.5%
ValueCountFrequency (%)
7201
< 0.1%
6981
< 0.1%
6941
< 0.1%
6931
< 0.1%
6641
< 0.1%
6601
< 0.1%
6481
< 0.1%
6461
< 0.1%
6361
< 0.1%
6291
< 0.1%

dcom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct632
Distinct (%)0.5%
Missing192199
Missing (%)61.5%
Infinite0
Infinite (%)0.0%
Mean147.2092161
Minimum0
Maximum720
Zeros1903
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:45:07.353165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q128
median117
Q3237
95-th percentile408
Maximum720
Range720
Interquartile range (IQR)209

Descriptive statistics

Standard deviation134.690594
Coefficient of variation (CV)0.9149603369
Kurtosis-0.1164397873
Mean147.2092161
Median Absolute Deviation (MAD)96
Skewness0.8606765904
Sum17746071
Variance18141.5561
MonotonicityNot monotonic
2021-12-08T12:45:07.492349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01903
 
0.6%
11875
 
0.6%
31650
 
0.5%
41611
 
0.5%
481590
 
0.5%
71585
 
0.5%
601542
 
0.5%
361528
 
0.5%
21497
 
0.5%
1321412
 
0.5%
Other values (622)104357
33.4%
(Missing)192199
61.5%
ValueCountFrequency (%)
01903
0.6%
11875
0.6%
21497
0.5%
31650
0.5%
41611
0.5%
51336
0.4%
61314
0.4%
71585
0.5%
81250
0.4%
91215
0.4%
ValueCountFrequency (%)
72012
< 0.1%
7181
 
< 0.1%
7161
 
< 0.1%
7082
 
< 0.1%
6981
 
< 0.1%
6963
 
< 0.1%
6931
 
< 0.1%
6871
 
< 0.1%
6843
 
< 0.1%
6726
< 0.1%

proest
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192568 
28
 
9229
08
 
7216
15
 
5191
36
 
4913
Other values (48)
93632 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row
3rd row
4th row01
5th row

Common Values

ValueCountFrequency (%)
192568
61.6%
289229
 
3.0%
087216
 
2.3%
155191
 
1.7%
364913
 
1.6%
464858
 
1.6%
414700
 
1.5%
303920
 
1.3%
073356
 
1.1%
503239
 
1.0%
Other values (43)73559
 
23.5%

Length

2021-12-08T12:45:07.811793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
289229
 
7.7%
087216
 
6.0%
155191
 
4.3%
364913
 
4.1%
464858
 
4.0%
414700
 
3.9%
303920
 
3.3%
073356
 
2.8%
503239
 
2.7%
333182
 
2.6%
Other values (42)70377
58.6%

Most occurring characters

ValueCountFrequency (%)
385136
61.6%
337722
 
6.0%
134253
 
5.5%
033085
 
5.3%
232685
 
5.2%
429591
 
4.7%
823680
 
3.8%
515301
 
2.4%
615100
 
2.4%
710985
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator385136
61.6%
Decimal Number240362
38.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
337722
15.7%
134253
14.3%
033085
13.8%
232685
13.6%
429591
12.3%
823680
9.9%
515301
6.4%
615100
6.3%
710985
 
4.6%
97960
 
3.3%
Space Separator
ValueCountFrequency (%)
385136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
385136
61.6%
337722
 
6.0%
134253
 
5.5%
033085
 
5.3%
232685
 
5.2%
429591
 
4.7%
823680
 
3.8%
515301
 
2.4%
615100
 
2.4%
710985
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
385136
61.6%
337722
 
6.0%
134253
 
5.5%
033085
 
5.3%
232685
 
5.2%
429591
 
4.7%
823680
 
3.8%
515301
 
2.4%
615100
 
2.4%
710985
 
1.8%

regest
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
312380 
115
 
152
600
 
44
610
 
41
100
 
31
Other values (12)
 
101

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters938247
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
312380
99.9%
115152
 
< 0.1%
60044
 
< 0.1%
61041
 
< 0.1%
10031
 
< 0.1%
62027
 
< 0.1%
30017
 
< 0.1%
35012
 
< 0.1%
3108
 
< 0.1%
1258
 
< 0.1%
Other values (7)29
 
< 0.1%

Length

2021-12-08T12:45:08.034369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
115152
41.2%
60044
 
11.9%
61041
 
11.1%
10031
 
8.4%
62027
 
7.3%
30017
 
4.6%
35012
 
3.3%
3108
 
2.2%
1258
 
2.2%
5007
 
1.9%
Other values (6)22
 
6.0%

Most occurring characters

ValueCountFrequency (%)
937140
99.9%
1402
 
< 0.1%
0313
 
< 0.1%
5179
 
< 0.1%
6115
 
< 0.1%
245
 
< 0.1%
340
 
< 0.1%
410
 
< 0.1%
83
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator937140
99.9%
Decimal Number1107
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1402
36.3%
0313
28.3%
5179
16.2%
6115
 
10.4%
245
 
4.1%
340
 
3.6%
410
 
0.9%
83
 
0.3%
Space Separator
ValueCountFrequency (%)
937140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common938247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
937140
99.9%
1402
 
< 0.1%
0313
 
< 0.1%
5179
 
< 0.1%
6115
 
< 0.1%
245
 
< 0.1%
340
 
< 0.1%
410
 
< 0.1%
83
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII938247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
937140
99.9%
1402
 
< 0.1%
0313
 
< 0.1%
5179
 
< 0.1%
6115
 
< 0.1%
245
 
< 0.1%
340
 
< 0.1%
410
 
< 0.1%
83
 
< 0.1%

parco1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
1
103238 
6
 
17312

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row
4th row6
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
1103238
33.0%
617312
 
5.5%

Length

2021-12-08T12:45:08.232045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:08.295672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1103238
85.6%
617312
 
14.4%

Most occurring characters

ValueCountFrequency (%)
192199
61.5%
1103238
33.0%
617312
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator192199
61.5%
Decimal Number120550
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1103238
85.6%
617312
 
14.4%
Space Separator
ValueCountFrequency (%)
192199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192199
61.5%
1103238
33.0%
617312
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192199
61.5%
1103238
33.0%
617312
 
5.5%

parco2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
295437 
05
 
8634
03
 
2195
07
 
2032
06
 
1802
Other values (4)
 
2649

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row05
5th row

Common Values

ValueCountFrequency (%)
295437
94.5%
058634
 
2.8%
032195
 
0.7%
072032
 
0.6%
061802
 
0.6%
011209
 
0.4%
041136
 
0.4%
02246
 
0.1%
0058
 
< 0.1%

Length

2021-12-08T12:45:08.477803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:08.552002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
058634
49.9%
032195
 
12.7%
072032
 
11.7%
061802
 
10.4%
011209
 
7.0%
041136
 
6.6%
02246
 
1.4%
0058
 
0.3%

Most occurring characters

ValueCountFrequency (%)
590874
94.5%
017370
 
2.8%
58634
 
1.4%
32195
 
0.4%
72032
 
0.3%
61802
 
0.3%
11209
 
0.2%
41136
 
0.2%
2246
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator590874
94.5%
Decimal Number34624
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017370
50.2%
58634
24.9%
32195
 
6.3%
72032
 
5.9%
61802
 
5.2%
11209
 
3.5%
41136
 
3.3%
2246
 
0.7%
Space Separator
ValueCountFrequency (%)
590874
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
590874
94.5%
017370
 
2.8%
58634
 
1.4%
32195
 
0.4%
72032
 
0.3%
61802
 
0.3%
11209
 
0.2%
41136
 
0.2%
2246
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
590874
94.5%
017370
 
2.8%
58634
 
1.4%
32195
 
0.4%
72032
 
0.3%
61802
 
0.3%
11209
 
0.2%
41136
 
0.2%
2246
 
< 0.1%

horasp
Categorical

HIGH CARDINALITY

Distinct287
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
213109 
4000
53009 
3730
 
13709
3500
 
7103
2000
 
4880
Other values (282)
 
20939

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1250996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)< 0.1%

Sample

1st row4000
2nd row
3rd row
4th row2500
5th row

Common Values

ValueCountFrequency (%)
213109
68.1%
400053009
 
16.9%
373013709
 
4.4%
35007103
 
2.3%
20004880
 
1.6%
30003367
 
1.1%
99992332
 
0.7%
25001605
 
0.5%
1500807
 
0.3%
3700756
 
0.2%
Other values (277)12072
 
3.9%

Length

2021-12-08T12:45:08.786173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
400053009
53.2%
373013709
 
13.8%
35007103
 
7.1%
20004880
 
4.9%
30003367
 
3.4%
99992332
 
2.3%
25001605
 
1.6%
1500807
 
0.8%
3700756
 
0.8%
1000753
 
0.8%
Other values (276)11319
 
11.4%

Most occurring characters

ValueCountFrequency (%)
852436
68.1%
0242940
 
19.4%
455980
 
4.5%
344623
 
3.6%
715157
 
1.2%
511157
 
0.9%
910493
 
0.8%
210398
 
0.8%
13692
 
0.3%
82091
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator852436
68.1%
Decimal Number398560
31.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0242940
61.0%
455980
 
14.0%
344623
 
11.2%
715157
 
3.8%
511157
 
2.8%
910493
 
2.6%
210398
 
2.6%
13692
 
0.9%
82091
 
0.5%
62029
 
0.5%
Space Separator
ValueCountFrequency (%)
852436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1250996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
852436
68.1%
0242940
 
19.4%
455980
 
4.5%
344623
 
3.6%
715157
 
1.2%
511157
 
0.9%
910493
 
0.8%
210398
 
0.8%
13692
 
0.3%
82091
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
852436
68.1%
0242940
 
19.4%
455980
 
4.5%
344623
 
3.6%
715157
 
1.2%
511157
 
0.9%
910493
 
0.8%
210398
 
0.8%
13692
 
0.3%
82091
 
0.2%

horash
Categorical

HIGH CARDINALITY

Distinct345
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
4000
55540 
3730
 
12512
3500
 
7914
9999
 
6584
Other values (340)
38000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1250996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)< 0.1%

Sample

1st row4000
2nd row
3rd row
4th row2500
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
400055540
 
17.8%
373012512
 
4.0%
35007914
 
2.5%
99996584
 
2.1%
20005377
 
1.7%
30004129
 
1.3%
50004008
 
1.3%
45002993
 
1.0%
60001948
 
0.6%
Other values (335)19545
 
6.2%

Length

2021-12-08T12:45:09.046922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
400055540
46.1%
373012512
 
10.4%
35007914
 
6.6%
99996584
 
5.5%
20005377
 
4.5%
30004129
 
3.4%
50004008
 
3.3%
45002993
 
2.5%
60001948
 
1.6%
25001881
 
1.6%
Other values (334)17664
 
14.7%

Most occurring characters

ValueCountFrequency (%)
768796
61.5%
0287390
 
23.0%
463080
 
5.0%
344774
 
3.6%
927749
 
2.2%
520297
 
1.6%
715147
 
1.2%
212177
 
1.0%
64475
 
0.4%
14159
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator768796
61.5%
Decimal Number482200
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0287390
59.6%
463080
 
13.1%
344774
 
9.3%
927749
 
5.8%
520297
 
4.2%
715147
 
3.1%
212177
 
2.5%
64475
 
0.9%
14159
 
0.9%
82952
 
0.6%
Space Separator
ValueCountFrequency (%)
768796
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1250996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
768796
61.5%
0287390
 
23.0%
463080
 
5.0%
344774
 
3.6%
927749
 
2.2%
520297
 
1.6%
715147
 
1.2%
212177
 
1.0%
64475
 
0.4%
14159
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
768796
61.5%
0287390
 
23.0%
463080
 
5.0%
344774
 
3.6%
927749
 
2.2%
520297
 
1.6%
715147
 
1.2%
212177
 
1.0%
64475
 
0.4%
14159
 
0.3%

horase
Categorical

HIGH CARDINALITY

Distinct365
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
4000
39210 
0000
 
17579
3730
 
8884
9999
 
6132
Other values (360)
48745 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1250996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)< 0.1%

Sample

1st row4000
2nd row
3rd row
4th row2000
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
400039210
 
12.5%
000017579
 
5.6%
37308884
 
2.8%
99996132
 
2.0%
35006116
 
2.0%
20004881
 
1.6%
30004213
 
1.3%
32003257
 
1.0%
50003235
 
1.0%
Other values (355)27043
 
8.6%

Length

2021-12-08T12:45:09.316450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
400039210
32.5%
000017579
14.6%
37308884
 
7.4%
99996132
 
5.1%
35006116
 
5.1%
20004881
 
4.0%
30004213
 
3.5%
32003257
 
2.7%
50003235
 
2.7%
45002369
 
2.0%
Other values (354)24674
20.5%

Most occurring characters

ValueCountFrequency (%)
768796
61.5%
0309452
24.7%
448397
 
3.9%
338546
 
3.1%
925921
 
2.1%
217271
 
1.4%
517260
 
1.4%
711473
 
0.9%
15468
 
0.4%
64892
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator768796
61.5%
Decimal Number482200
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0309452
64.2%
448397
 
10.0%
338546
 
8.0%
925921
 
5.4%
217271
 
3.6%
517260
 
3.6%
711473
 
2.4%
15468
 
1.1%
64892
 
1.0%
83520
 
0.7%
Space Separator
ValueCountFrequency (%)
768796
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1250996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
768796
61.5%
0309452
24.7%
448397
 
3.9%
338546
 
3.1%
925921
 
2.1%
217271
 
1.4%
517260
 
1.4%
711473
 
0.9%
15468
 
0.4%
64892
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
768796
61.5%
0309452
24.7%
448397
 
3.9%
338546
 
3.1%
925921
 
2.1%
217271
 
1.4%
517260
 
1.4%
711473
 
0.9%
15468
 
0.4%
64892
 
0.4%

extra
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
213047 
6
95460 
1
 
4242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row
3rd row
4th row6
5th row

Common Values

ValueCountFrequency (%)
213047
68.1%
695460
30.5%
14242
 
1.4%

Length

2021-12-08T12:45:09.493503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:09.558113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
695460
95.7%
14242
 
4.3%

Most occurring characters

ValueCountFrequency (%)
213047
68.1%
695460
30.5%
14242
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator213047
68.1%
Decimal Number99702
31.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
695460
95.7%
14242
 
4.3%
Space Separator
ValueCountFrequency (%)
213047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
213047
68.1%
695460
30.5%
14242
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
213047
68.1%
695460
30.5%
14242
 
1.4%

extpag
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
308507 
0000
 
1986
0500
 
306
1000
 
278
9999
 
275
Other values (66)
 
1397

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1250996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
308507
98.6%
00001986
 
0.6%
0500306
 
0.1%
1000278
 
0.1%
9999275
 
0.1%
0800225
 
0.1%
0200175
 
0.1%
0400162
 
0.1%
0300109
 
< 0.1%
0600106
 
< 0.1%
Other values (61)620
 
0.2%

Length

2021-12-08T12:45:09.780580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00001986
46.8%
0500306
 
7.2%
1000278
 
6.6%
9999275
 
6.5%
0800225
 
5.3%
0200175
 
4.1%
0400162
 
3.8%
0300109
 
2.6%
0600106
 
2.5%
200065
 
1.5%
Other values (60)555
 
13.1%

Most occurring characters

ValueCountFrequency (%)
1234028
98.6%
013440
 
1.1%
91156
 
0.1%
1570
 
< 0.1%
2408
 
< 0.1%
5393
 
< 0.1%
8255
 
< 0.1%
4249
 
< 0.1%
3231
 
< 0.1%
6162
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator1234028
98.6%
Decimal Number16968
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013440
79.2%
91156
 
6.8%
1570
 
3.4%
2408
 
2.4%
5393
 
2.3%
8255
 
1.5%
4249
 
1.5%
3231
 
1.4%
6162
 
1.0%
7104
 
0.6%
Space Separator
ValueCountFrequency (%)
1234028
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1250996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1234028
98.6%
013440
 
1.1%
91156
 
0.1%
1570
 
< 0.1%
2408
 
< 0.1%
5393
 
< 0.1%
8255
 
< 0.1%
4249
 
< 0.1%
3231
 
< 0.1%
6162
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1234028
98.6%
013440
 
1.1%
91156
 
0.1%
1570
 
< 0.1%
2408
 
< 0.1%
5393
 
< 0.1%
8255
 
< 0.1%
4249
 
< 0.1%
3231
 
< 0.1%
6162
 
< 0.1%

extnpg
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
308507 
0000
 
2048
1000
 
385
0500
 
347
9999
 
287
Other values (75)
 
1175

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1250996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
308507
98.6%
00002048
 
0.7%
1000385
 
0.1%
0500347
 
0.1%
9999287
 
0.1%
0300133
 
< 0.1%
0400120
 
< 0.1%
2000117
 
< 0.1%
0200116
 
< 0.1%
150097
 
< 0.1%
Other values (70)592
 
0.2%

Length

2021-12-08T12:45:10.021369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00002048
48.3%
1000385
 
9.1%
0500347
 
8.2%
9999287
 
6.8%
0300133
 
3.1%
0400120
 
2.8%
2000117
 
2.8%
0200116
 
2.7%
150097
 
2.3%
080097
 
2.3%
Other values (69)495
 
11.7%

Most occurring characters

ValueCountFrequency (%)
1234028
98.6%
013453
 
1.1%
91184
 
0.1%
1648
 
0.1%
5494
 
< 0.1%
2377
 
< 0.1%
3337
 
< 0.1%
4177
 
< 0.1%
8120
 
< 0.1%
690
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator1234028
98.6%
Decimal Number16968
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013453
79.3%
91184
 
7.0%
1648
 
3.8%
5494
 
2.9%
2377
 
2.2%
3337
 
2.0%
4177
 
1.0%
8120
 
0.7%
690
 
0.5%
788
 
0.5%
Space Separator
ValueCountFrequency (%)
1234028
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1250996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1234028
98.6%
013453
 
1.1%
91184
 
0.1%
1648
 
0.1%
5494
 
< 0.1%
2377
 
< 0.1%
3337
 
< 0.1%
4177
 
< 0.1%
8120
 
< 0.1%
690
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1234028
98.6%
013453
 
1.1%
91184
 
0.1%
1648
 
0.1%
5494
 
< 0.1%
2377
 
< 0.1%
3337
 
< 0.1%
4177
 
< 0.1%
8120
 
< 0.1%
690
 
< 0.1%

rzdifh
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
294363 
03
 
6958
10
 
2653
17
 
1918
18
 
1497
Other values (16)
 
5360

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row01
5th row

Common Values

ValueCountFrequency (%)
294363
94.1%
036958
 
2.2%
102653
 
0.8%
171918
 
0.6%
181497
 
0.5%
161218
 
0.4%
051015
 
0.3%
11872
 
0.3%
01698
 
0.2%
15661
 
0.2%
Other values (11)896
 
0.3%

Length

2021-12-08T12:45:10.244847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
036958
37.8%
102653
 
14.4%
171918
 
10.4%
181497
 
8.1%
161218
 
6.6%
051015
 
5.5%
11872
 
4.7%
01698
 
3.8%
15661
 
3.6%
14268
 
1.5%
Other values (10)628
 
3.4%

Most occurring characters

ValueCountFrequency (%)
588726
94.1%
011936
 
1.9%
110685
 
1.7%
36968
 
1.1%
72032
 
0.3%
81700
 
0.3%
51676
 
0.3%
61219
 
0.2%
4402
 
0.1%
9101
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator588726
94.1%
Decimal Number36772
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011936
32.5%
110685
29.1%
36968
18.9%
72032
 
5.5%
81700
 
4.6%
51676
 
4.6%
61219
 
3.3%
4402
 
1.1%
9101
 
0.3%
253
 
0.1%
Space Separator
ValueCountFrequency (%)
588726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
588726
94.1%
011936
 
1.9%
110685
 
1.7%
36968
 
1.1%
72032
 
0.3%
81700
 
0.3%
51676
 
0.3%
61219
 
0.2%
4402
 
0.1%
9101
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
588726
94.1%
011936
 
1.9%
110685
 
1.7%
36968
 
1.1%
72032
 
0.3%
81700
 
0.3%
51676
 
0.3%
61219
 
0.2%
4402
 
0.1%
9101
 
< 0.1%

traplu
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
6
117703 
1
 
2847

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row
3rd row
4th row6
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
6117703
37.6%
12847
 
0.9%

Length

2021-12-08T12:45:10.440627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:10.504255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
6117703
97.6%
12847
 
2.4%

Most occurring characters

ValueCountFrequency (%)
192199
61.5%
6117703
37.6%
12847
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator192199
61.5%
Decimal Number120550
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6117703
97.6%
12847
 
2.4%
Space Separator
ValueCountFrequency (%)
192199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192199
61.5%
6117703
37.6%
12847
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192199
61.5%
6117703
37.6%
12847
 
0.9%

ocuplu1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
309902 
2
 
950
9
 
500
5
 
484
3
 
268
Other values (6)
 
645

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
309902
99.1%
2950
 
0.3%
9500
 
0.2%
5484
 
0.2%
3268
 
0.1%
1176
 
0.1%
6160
 
0.1%
4149
 
< 0.1%
787
 
< 0.1%
872
 
< 0.1%

Length

2021-12-08T12:45:10.675748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2950
33.4%
9500
17.6%
5484
17.0%
3268
 
9.4%
1176
 
6.2%
6160
 
5.6%
4149
 
5.2%
787
 
3.1%
872
 
2.5%
01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
309902
99.1%
2950
 
0.3%
9500
 
0.2%
5484
 
0.2%
3268
 
0.1%
1176
 
0.1%
6160
 
0.1%
4149
 
< 0.1%
787
 
< 0.1%
872
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator309902
99.1%
Decimal Number2847
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2950
33.4%
9500
17.6%
5484
17.0%
3268
 
9.4%
1176
 
6.2%
6160
 
5.6%
4149
 
5.2%
787
 
3.1%
872
 
2.5%
01
 
< 0.1%
Space Separator
ValueCountFrequency (%)
309902
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
309902
99.1%
2950
 
0.3%
9500
 
0.2%
5484
 
0.2%
3268
 
0.1%
1176
 
0.1%
6160
 
0.1%
4149
 
< 0.1%
787
 
< 0.1%
872
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
309902
99.1%
2950
 
0.3%
9500
 
0.2%
5484
 
0.2%
3268
 
0.1%
1176
 
0.1%
6160
 
0.1%
4149
 
< 0.1%
787
 
< 0.1%
872
 
< 0.1%

actplu1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
309902 
8
 
881
5
 
503
9
 
499
7
 
442
Other values (6)
 
522

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
309902
99.1%
8881
 
0.3%
5503
 
0.2%
9499
 
0.2%
7442
 
0.1%
0202
 
0.1%
6131
 
< 0.1%
468
 
< 0.1%
148
 
< 0.1%
245
 
< 0.1%

Length

2021-12-08T12:45:10.872552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8881
30.9%
5503
17.7%
9499
17.5%
7442
15.5%
0202
 
7.1%
6131
 
4.6%
468
 
2.4%
148
 
1.7%
245
 
1.6%
328
 
1.0%

Most occurring characters

ValueCountFrequency (%)
309902
99.1%
8881
 
0.3%
5503
 
0.2%
9499
 
0.2%
7442
 
0.1%
0202
 
0.1%
6131
 
< 0.1%
468
 
< 0.1%
148
 
< 0.1%
245
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator309902
99.1%
Decimal Number2847
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8881
30.9%
5503
17.7%
9499
17.5%
7442
15.5%
0202
 
7.1%
6131
 
4.6%
468
 
2.4%
148
 
1.7%
245
 
1.6%
328
 
1.0%
Space Separator
ValueCountFrequency (%)
309902
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
309902
99.1%
8881
 
0.3%
5503
 
0.2%
9499
 
0.2%
7442
 
0.1%
0202
 
0.1%
6131
 
< 0.1%
468
 
< 0.1%
148
 
< 0.1%
245
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
309902
99.1%
8881
 
0.3%
5503
 
0.2%
9499
 
0.2%
7442
 
0.1%
0202
 
0.1%
6131
 
< 0.1%
468
 
< 0.1%
148
 
< 0.1%
245
 
< 0.1%

sitplu
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
309902 
08
 
1599
03
 
720
07
 
329
01
 
141
Other values (3)
 
58

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
309902
99.1%
081599
 
0.5%
03720
 
0.2%
07329
 
0.1%
01141
 
< 0.1%
0634
 
< 0.1%
0917
 
< 0.1%
057
 
< 0.1%

Length

2021-12-08T12:45:11.067354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:11.136951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
081599
56.2%
03720
25.3%
07329
 
11.6%
01141
 
5.0%
0634
 
1.2%
0917
 
0.6%
057
 
0.2%

Most occurring characters

ValueCountFrequency (%)
619804
99.1%
02847
 
0.5%
81599
 
0.3%
3720
 
0.1%
7329
 
0.1%
1141
 
< 0.1%
634
 
< 0.1%
917
 
< 0.1%
57
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator619804
99.1%
Decimal Number5694
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02847
50.0%
81599
28.1%
3720
 
12.6%
7329
 
5.8%
1141
 
2.5%
634
 
0.6%
917
 
0.3%
57
 
0.1%
Space Separator
ValueCountFrequency (%)
619804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
619804
99.1%
02847
 
0.5%
81599
 
0.3%
3720
 
0.1%
7329
 
0.1%
1141
 
< 0.1%
634
 
< 0.1%
917
 
< 0.1%
57
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
619804
99.1%
02847
 
0.5%
81599
 
0.3%
3720
 
0.1%
7329
 
0.1%
1141
 
< 0.1%
634
 
< 0.1%
917
 
< 0.1%
57
 
< 0.1%

horplu
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
309902 
0000
 
470
1000
 
312
2000
 
259
1500
 
165
Other values (91)
 
1641

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1250996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
309902
99.1%
0000470
 
0.2%
1000312
 
0.1%
2000259
 
0.1%
1500165
 
0.1%
0400145
 
< 0.1%
0800130
 
< 0.1%
0500115
 
< 0.1%
9999111
 
< 0.1%
020095
 
< 0.1%
Other values (86)1045
 
0.3%

Length

2021-12-08T12:45:11.374088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0000470
16.5%
1000312
 
11.0%
2000259
 
9.1%
1500165
 
5.8%
0400145
 
5.1%
0800130
 
4.6%
0500115
 
4.0%
9999111
 
3.9%
020095
 
3.3%
060090
 
3.2%
Other values (85)955
33.5%

Most occurring characters

ValueCountFrequency (%)
1239608
99.1%
07827
 
0.6%
1842
 
0.1%
2675
 
0.1%
9503
 
< 0.1%
3400
 
< 0.1%
5396
 
< 0.1%
4294
 
< 0.1%
8192
 
< 0.1%
6165
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator1239608
99.1%
Decimal Number11388
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07827
68.7%
1842
 
7.4%
2675
 
5.9%
9503
 
4.4%
3400
 
3.5%
5396
 
3.5%
4294
 
2.6%
8192
 
1.7%
6165
 
1.4%
794
 
0.8%
Space Separator
ValueCountFrequency (%)
1239608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1250996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1239608
99.1%
07827
 
0.6%
1842
 
0.1%
2675
 
0.1%
9503
 
< 0.1%
3400
 
< 0.1%
5396
 
< 0.1%
4294
 
< 0.1%
8192
 
< 0.1%
6165
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1239608
99.1%
07827
 
0.6%
1842
 
0.1%
2675
 
0.1%
9503
 
< 0.1%
3400
 
< 0.1%
5396
 
< 0.1%
4294
 
< 0.1%
8192
 
< 0.1%
6165
 
< 0.1%

mashor
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
3
104531 
1
 
11573
2
 
4446

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row
3rd row
4th row1
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
3104531
33.4%
111573
 
3.7%
24446
 
1.4%

Length

2021-12-08T12:45:11.578881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:11.643510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3104531
86.7%
111573
 
9.6%
24446
 
3.7%

Most occurring characters

ValueCountFrequency (%)
192199
61.5%
3104531
33.4%
111573
 
3.7%
24446
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator192199
61.5%
Decimal Number120550
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3104531
86.7%
111573
 
9.6%
24446
 
3.7%
Space Separator
ValueCountFrequency (%)
192199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192199
61.5%
3104531
33.4%
111573
 
3.7%
24446
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192199
61.5%
3104531
33.4%
111573
 
3.7%
24446
 
1.4%

dismas
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
301176 
1
 
10614
6
 
959

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row1
5th row

Common Values

ValueCountFrequency (%)
301176
96.3%
110614
 
3.4%
6959
 
0.3%

Length

2021-12-08T12:45:11.822351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:11.888287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
110614
91.7%
6959
 
8.3%

Most occurring characters

ValueCountFrequency (%)
301176
96.3%
110614
 
3.4%
6959
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator301176
96.3%
Decimal Number11573
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110614
91.7%
6959
 
8.3%
Space Separator
ValueCountFrequency (%)
301176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301176
96.3%
110614
 
3.4%
6959
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301176
96.3%
110614
 
3.4%
6959
 
0.3%

rzndish
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
311977 
02
 
230
04
 
210
05
 
142
03
 
138

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
311977
99.8%
02230
 
0.1%
04210
 
0.1%
05142
 
< 0.1%
03138
 
< 0.1%
0152
 
< 0.1%

Length

2021-12-08T12:45:12.052761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:12.119387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
02230
29.8%
04210
27.2%
05142
18.4%
03138
17.9%
0152
 
6.7%

Most occurring characters

ValueCountFrequency (%)
623954
99.8%
0772
 
0.1%
2230
 
< 0.1%
4210
 
< 0.1%
5142
 
< 0.1%
3138
 
< 0.1%
152
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator623954
99.8%
Decimal Number1544
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0772
50.0%
2230
 
14.9%
4210
 
13.6%
5142
 
9.2%
3138
 
8.9%
152
 
3.4%
Space Separator
ValueCountFrequency (%)
623954
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
623954
99.8%
0772
 
0.1%
2230
 
< 0.1%
4210
 
< 0.1%
5142
 
< 0.1%
3138
 
< 0.1%
152
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
623954
99.8%
0772
 
0.1%
2230
 
< 0.1%
4210
 
< 0.1%
5142
 
< 0.1%
3138
 
< 0.1%
152
 
< 0.1%

hordes
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
296730 
40
 
7712
35
 
1558
30
 
1541
50
 
846
Other values (73)
 
4362

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row40
5th row

Common Values

ValueCountFrequency (%)
296730
94.9%
407712
 
2.5%
351558
 
0.5%
301541
 
0.5%
50846
 
0.3%
99773
 
0.2%
45697
 
0.2%
20662
 
0.2%
25392
 
0.1%
37272
 
0.1%
Other values (68)1566
 
0.5%

Length

2021-12-08T12:45:12.351186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
407712
48.1%
351558
 
9.7%
301541
 
9.6%
50846
 
5.3%
99773
 
4.8%
45697
 
4.4%
20662
 
4.1%
25392
 
2.4%
37272
 
1.7%
32150
 
0.9%
Other values (67)1416
 
8.8%

Most occurring characters

ValueCountFrequency (%)
593460
94.9%
011087
 
1.8%
48934
 
1.4%
33865
 
0.6%
53801
 
0.6%
91633
 
0.3%
21469
 
0.2%
7413
 
0.1%
6351
 
0.1%
8273
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator593460
94.9%
Decimal Number32038
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011087
34.6%
48934
27.9%
33865
 
12.1%
53801
 
11.9%
91633
 
5.1%
21469
 
4.6%
7413
 
1.3%
6351
 
1.1%
8273
 
0.9%
1212
 
0.7%
Space Separator
ValueCountFrequency (%)
593460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
593460
94.9%
011087
 
1.8%
48934
 
1.4%
33865
 
0.6%
53801
 
0.6%
91633
 
0.3%
21469
 
0.2%
7413
 
0.1%
6351
 
0.1%
8273
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
593460
94.9%
011087
 
1.8%
48934
 
1.4%
33865
 
0.6%
53801
 
0.6%
91633
 
0.3%
21469
 
0.2%
7413
 
0.1%
6351
 
0.1%
8273
 
< 0.1%

busotr
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192199 
6
115573 
1
 
4977

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row
3rd row
4th row6
5th row

Common Values

ValueCountFrequency (%)
192199
61.5%
6115573
37.0%
14977
 
1.6%

Length

2021-12-08T12:45:12.551317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:12.614861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
6115573
95.9%
14977
 
4.1%

Most occurring characters

ValueCountFrequency (%)
192199
61.5%
6115573
37.0%
14977
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator192199
61.5%
Decimal Number120550
38.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6115573
95.9%
14977
 
4.1%
Space Separator
ValueCountFrequency (%)
192199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192199
61.5%
6115573
37.0%
14977
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192199
61.5%
6115573
37.0%
14977
 
1.6%

busca
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
202645 
6
91047 
1
 
19057

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row6
3rd row
4th row
5th row1

Common Values

ValueCountFrequency (%)
202645
64.8%
691047
29.1%
119057
 
6.1%

Length

2021-12-08T12:45:12.789414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:12.855024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
691047
82.7%
119057
 
17.3%

Most occurring characters

ValueCountFrequency (%)
202645
64.8%
691047
29.1%
119057
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator202645
64.8%
Decimal Number110104
35.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
691047
82.7%
119057
 
17.3%
Space Separator
ValueCountFrequency (%)
202645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
202645
64.8%
691047
29.1%
119057
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
202645
64.8%
691047
29.1%
119057
 
6.1%

desea
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
224024 
6
77875 
1
 
10850

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row6
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
224024
71.6%
677875
 
24.9%
110850
 
3.5%

Length

2021-12-08T12:45:13.022520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:13.089117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
677875
87.8%
110850
 
12.2%

Most occurring characters

ValueCountFrequency (%)
224024
71.6%
677875
 
24.9%
110850
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator224024
71.6%
Decimal Number88725
 
28.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
677875
87.8%
110850
 
12.2%
Space Separator
ValueCountFrequency (%)
224024
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
224024
71.6%
677875
 
24.9%
110850
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
224024
71.6%
677875
 
24.9%
110850
 
3.5%

fobact
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
288673 
1
 
23479
6
 
597

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row1

Common Values

ValueCountFrequency (%)
288673
92.3%
123479
 
7.5%
6597
 
0.2%

Length

2021-12-08T12:45:13.271264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:13.336889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
123479
97.5%
6597
 
2.5%

Most occurring characters

ValueCountFrequency (%)
288673
92.3%
123479
 
7.5%
6597
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator288673
92.3%
Decimal Number24076
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123479
97.5%
6597
 
2.5%
Space Separator
ValueCountFrequency (%)
288673
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
288673
92.3%
123479
 
7.5%
6597
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288673
92.3%
123479
 
7.5%
6597
 
0.2%

nbusca
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
221949 
07
29612 
06
 
18772
08
 
16316
03
 
12002
Other values (5)
 
14098

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row03
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
221949
71.0%
0729612
 
9.5%
0618772
 
6.0%
0816316
 
5.2%
0312002
 
3.8%
058251
 
2.6%
043231
 
1.0%
012040
 
0.7%
02437
 
0.1%
00139
 
< 0.1%

Length

2021-12-08T12:45:13.512495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:13.583619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0729612
32.6%
0618772
20.7%
0816316
18.0%
0312002
13.2%
058251
 
9.1%
043231
 
3.6%
012040
 
2.2%
02437
 
0.5%
00139
 
0.2%

Most occurring characters

ValueCountFrequency (%)
443898
71.0%
090939
 
14.5%
729612
 
4.7%
618772
 
3.0%
816316
 
2.6%
312002
 
1.9%
58251
 
1.3%
43231
 
0.5%
12040
 
0.3%
2437
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator443898
71.0%
Decimal Number181600
29.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
090939
50.1%
729612
 
16.3%
618772
 
10.3%
816316
 
9.0%
312002
 
6.6%
58251
 
4.5%
43231
 
1.8%
12040
 
1.1%
2437
 
0.2%
Space Separator
ValueCountFrequency (%)
443898
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
443898
71.0%
090939
 
14.5%
729612
 
4.7%
618772
 
3.0%
816316
 
2.6%
312002
 
1.9%
58251
 
1.3%
43231
 
0.5%
12040
 
0.3%
2437
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
443898
71.0%
090939
 
14.5%
729612
 
4.7%
618772
 
3.0%
816316
 
2.6%
312002
 
1.9%
58251
 
1.3%
43231
 
0.5%
12040
 
0.3%
2437
 
0.1%

asala
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
286393 
1
 
24765
0
 
1049
6
 
542

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row1

Common Values

ValueCountFrequency (%)
286393
91.6%
124765
 
7.9%
01049
 
0.3%
6542
 
0.2%

Length

2021-12-08T12:45:13.784442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:13.848054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
124765
94.0%
01049
 
4.0%
6542
 
2.1%

Most occurring characters

ValueCountFrequency (%)
286393
91.6%
124765
 
7.9%
01049
 
0.3%
6542
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator286393
91.6%
Decimal Number26356
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
124765
94.0%
01049
 
4.0%
6542
 
2.1%
Space Separator
ValueCountFrequency (%)
286393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
286393
91.6%
124765
 
7.9%
01049
 
0.3%
6542
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
286393
91.6%
124765
 
7.9%
01049
 
0.3%
6542
 
0.2%

embus
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
287984 
5
 
11378
1
 
7600
2
 
3070
3
 
2185
Other values (2)
 
532

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row5

Common Values

ValueCountFrequency (%)
287984
92.1%
511378
 
3.6%
17600
 
2.4%
23070
 
1.0%
32185
 
0.7%
4309
 
0.1%
0223
 
0.1%

Length

2021-12-08T12:45:14.028687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:14.098820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
511378
45.9%
17600
30.7%
23070
 
12.4%
32185
 
8.8%
4309
 
1.2%
0223
 
0.9%

Most occurring characters

ValueCountFrequency (%)
287984
92.1%
511378
 
3.6%
17600
 
2.4%
23070
 
1.0%
32185
 
0.7%
4309
 
0.1%
0223
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator287984
92.1%
Decimal Number24765
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
511378
45.9%
17600
30.7%
23070
 
12.4%
32185
 
8.8%
4309
 
1.2%
0223
 
0.9%
Space Separator
ValueCountFrequency (%)
287984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
287984
92.1%
511378
 
3.6%
17600
 
2.4%
23070
 
1.0%
32185
 
0.7%
4309
 
0.1%
0223
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
287984
92.1%
511378
 
3.6%
17600
 
2.4%
23070
 
1.0%
32185
 
0.7%
4309
 
0.1%
0223
 
0.1%

itbu
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
286393 
08
 
4841
02
 
4584
04
 
3892
03
 
3708
Other values (4)
 
9331

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row06

Common Values

ValueCountFrequency (%)
286393
91.6%
084841
 
1.5%
024584
 
1.5%
043892
 
1.2%
033708
 
1.2%
073188
 
1.0%
012543
 
0.8%
052158
 
0.7%
061442
 
0.5%

Length

2021-12-08T12:45:14.304634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:14.378404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
084841
18.4%
024584
17.4%
043892
14.8%
033708
14.1%
073188
12.1%
012543
9.6%
052158
8.2%
061442
 
5.5%

Most occurring characters

ValueCountFrequency (%)
572786
91.6%
026356
 
4.2%
84841
 
0.8%
24584
 
0.7%
43892
 
0.6%
33708
 
0.6%
73188
 
0.5%
12543
 
0.4%
52158
 
0.3%
61442
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator572786
91.6%
Decimal Number52712
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026356
50.0%
84841
 
9.2%
24584
 
8.7%
43892
 
7.4%
33708
 
7.0%
73188
 
6.0%
12543
 
4.8%
52158
 
4.1%
61442
 
2.7%
Space Separator
ValueCountFrequency (%)
572786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
572786
91.6%
026356
 
4.2%
84841
 
0.8%
24584
 
0.7%
43892
 
0.6%
33708
 
0.6%
73188
 
0.5%
12543
 
0.4%
52158
 
0.3%
61442
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
572786
91.6%
026356
 
4.2%
84841
 
0.8%
24584
 
0.7%
43892
 
0.6%
33708
 
0.6%
73188
 
0.5%
12543
 
0.4%
52158
 
0.3%
61442
 
0.2%

disp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
197666 
6
80818 
1
34265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row6
3rd row
4th row
5th row1

Common Values

ValueCountFrequency (%)
197666
63.2%
680818
25.8%
134265
 
11.0%

Length

2021-12-08T12:45:14.598353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:14.661403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
680818
70.2%
134265
29.8%

Most occurring characters

ValueCountFrequency (%)
197666
63.2%
680818
25.8%
134265
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator197666
63.2%
Decimal Number115083
36.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
680818
70.2%
134265
29.8%
Space Separator
ValueCountFrequency (%)
197666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
197666
63.2%
680818
25.8%
134265
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
197666
63.2%
680818
25.8%
134265
 
11.0%

rzndis
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
234388 
4
39033 
1
 
16680
3
 
11837
2
 
10235

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row3
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
234388
74.9%
439033
 
12.5%
116680
 
5.3%
311837
 
3.8%
210235
 
3.3%
5576
 
0.2%

Length

2021-12-08T12:45:14.818809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:14.885435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
439033
49.8%
116680
21.3%
311837
 
15.1%
210235
 
13.1%
5576
 
0.7%

Most occurring characters

ValueCountFrequency (%)
234388
74.9%
439033
 
12.5%
116680
 
5.3%
311837
 
3.8%
210235
 
3.3%
5576
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator234388
74.9%
Decimal Number78361
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
439033
49.8%
116680
21.3%
311837
 
15.1%
210235
 
13.1%
5576
 
0.7%
Space Separator
ValueCountFrequency (%)
234388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
234388
74.9%
439033
 
12.5%
116680
 
5.3%
311837
 
3.8%
210235
 
3.3%
5576
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
234388
74.9%
439033
 
12.5%
116680
 
5.3%
311837
 
3.8%
210235
 
3.3%
5576
 
0.2%

empant
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
168329 
1
110500 
6
33920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row1
3rd row
4th row
5th row6

Common Values

ValueCountFrequency (%)
168329
53.8%
1110500
35.3%
633920
 
10.8%

Length

2021-12-08T12:45:15.066580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:15.130162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1110500
76.5%
633920
 
23.5%

Most occurring characters

ValueCountFrequency (%)
168329
53.8%
1110500
35.3%
633920
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator168329
53.8%
Decimal Number144420
46.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1110500
76.5%
633920
 
23.5%
Space Separator
ValueCountFrequency (%)
168329
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
168329
53.8%
1110500
35.3%
633920
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
168329
53.8%
1110500
35.3%
633920
 
10.8%

dtant
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct813
Distinct (%)0.7%
Missing202249
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean146.1791041
Minimum0
Maximum840
Zeros886
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:45:15.219460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q120
median95
Q3214
95-th percentile528
Maximum840
Range840
Interquartile range (IQR)194

Descriptive statistics

Standard deviation164.0635597
Coefficient of variation (CV)1.122346185
Kurtosis2.6279512
Mean146.1791041
Median Absolute Deviation (MAD)84
Skewness1.661934524
Sum16152791
Variance26916.85163
MonotonicityNot monotonic
2021-12-08T12:45:15.340973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13023
 
1.0%
22847
 
0.9%
32800
 
0.9%
42155
 
0.7%
81728
 
0.6%
71693
 
0.5%
51683
 
0.5%
61525
 
0.5%
91505
 
0.5%
1201297
 
0.4%
Other values (803)90244
28.9%
(Missing)202249
64.7%
ValueCountFrequency (%)
0886
 
0.3%
13023
1.0%
22847
0.9%
32800
0.9%
42155
0.7%
51683
0.5%
61525
0.5%
71693
0.5%
81728
0.6%
91505
0.5%
ValueCountFrequency (%)
84097
< 0.1%
8391
 
< 0.1%
8382
 
< 0.1%
8371
 
< 0.1%
8321
 
< 0.1%
8314
 
< 0.1%
8302
 
< 0.1%
82826
 
< 0.1%
8271
 
< 0.1%
8251
 
< 0.1%

ocupa
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
289907 
5
 
6488
9
 
6323
7
 
2316
2
 
1957
Other values (6)
 
5758

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
289907
92.7%
56488
 
2.1%
96323
 
2.0%
72316
 
0.7%
21957
 
0.6%
41815
 
0.6%
31786
 
0.6%
81465
 
0.5%
6415
 
0.1%
1245
 
0.1%

Length

2021-12-08T12:45:16.225549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
56488
28.4%
96323
27.7%
72316
 
10.1%
21957
 
8.6%
41815
 
7.9%
31786
 
7.8%
81465
 
6.4%
6415
 
1.8%
1245
 
1.1%
032
 
0.1%

Most occurring characters

ValueCountFrequency (%)
289907
92.7%
56488
 
2.1%
96323
 
2.0%
72316
 
0.7%
21957
 
0.6%
41815
 
0.6%
31786
 
0.6%
81465
 
0.5%
6415
 
0.1%
1245
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator289907
92.7%
Decimal Number22842
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
56488
28.4%
96323
27.7%
72316
 
10.1%
21957
 
8.6%
41815
 
7.9%
31786
 
7.8%
81465
 
6.4%
6415
 
1.8%
1245
 
1.1%
032
 
0.1%
Space Separator
ValueCountFrequency (%)
289907
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
289907
92.7%
56488
 
2.1%
96323
 
2.0%
72316
 
0.7%
21957
 
0.6%
41815
 
0.6%
31786
 
0.6%
81465
 
0.5%
6415
 
0.1%
1245
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
289907
92.7%
56488
 
2.1%
96323
 
2.0%
72316
 
0.7%
21957
 
0.6%
41815
 
0.6%
31786
 
0.6%
81465
 
0.5%
6415
 
0.1%
1245
 
0.1%

acta
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
289907 
5
 
6984
8
 
4081
9
 
2539
0
 
2105
Other values (6)
 
7133

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
289907
92.7%
56984
 
2.2%
84081
 
1.3%
92539
 
0.8%
02105
 
0.7%
72077
 
0.7%
41395
 
0.4%
61210
 
0.4%
11027
 
0.3%
2791
 
0.3%

Length

2021-12-08T12:45:16.428950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
56984
30.6%
84081
17.9%
92539
 
11.1%
02105
 
9.2%
72077
 
9.1%
41395
 
6.1%
61210
 
5.3%
11027
 
4.5%
2791
 
3.5%
3633
 
2.8%

Most occurring characters

ValueCountFrequency (%)
289907
92.7%
56984
 
2.2%
84081
 
1.3%
92539
 
0.8%
02105
 
0.7%
72077
 
0.7%
41395
 
0.4%
61210
 
0.4%
11027
 
0.3%
2791
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator289907
92.7%
Decimal Number22842
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
56984
30.6%
84081
17.9%
92539
 
11.1%
02105
 
9.2%
72077
 
9.1%
41395
 
6.1%
61210
 
5.3%
11027
 
4.5%
2791
 
3.5%
3633
 
2.8%
Space Separator
ValueCountFrequency (%)
289907
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
289907
92.7%
56984
 
2.2%
84081
 
1.3%
92539
 
0.8%
02105
 
0.7%
72077
 
0.7%
41395
 
0.4%
61210
 
0.4%
11027
 
0.3%
2791
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
289907
92.7%
56984
 
2.2%
84081
 
1.3%
92539
 
0.8%
02105
 
0.7%
72077
 
0.7%
41395
 
0.4%
61210
 
0.4%
11027
 
0.3%
2791
 
0.3%

situa
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
289907 
08
 
18136
07
 
3159
03
 
1071
01
 
224
Other values (3)
 
252

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
289907
92.7%
0818136
 
5.8%
073159
 
1.0%
031071
 
0.3%
01224
 
0.1%
06162
 
0.1%
0969
 
< 0.1%
0521
 
< 0.1%

Length

2021-12-08T12:45:16.634763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:16.706386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0818136
79.4%
073159
 
13.8%
031071
 
4.7%
01224
 
1.0%
06162
 
0.7%
0969
 
0.3%
0521
 
0.1%

Most occurring characters

ValueCountFrequency (%)
579814
92.7%
022842
 
3.7%
818136
 
2.9%
73159
 
0.5%
31071
 
0.2%
1224
 
< 0.1%
6162
 
< 0.1%
969
 
< 0.1%
521
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator579814
92.7%
Decimal Number45684
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
022842
50.0%
818136
39.7%
73159
 
6.9%
31071
 
2.3%
1224
 
0.5%
6162
 
0.4%
969
 
0.2%
521
 
< 0.1%
Space Separator
ValueCountFrequency (%)
579814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
579814
92.7%
022842
 
3.7%
818136
 
2.9%
73159
 
0.5%
31071
 
0.2%
1224
 
< 0.1%
6162
 
< 0.1%
969
 
< 0.1%
521
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
579814
92.7%
022842
 
3.7%
818136
 
2.9%
73159
 
0.5%
31071
 
0.2%
1224
 
< 0.1%
6162
 
< 0.1%
969
 
< 0.1%
521
 
< 0.1%

ofemp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
3
190753 
82216 
2
21461 
1
 
16584
4
 
1735

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3190753
61.0%
82216
26.3%
221461
 
6.9%
116584
 
5.3%
41735
 
0.6%

Length

2021-12-08T12:45:16.918873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:16.987450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3190753
82.7%
221461
 
9.3%
116584
 
7.2%
41735
 
0.8%

Most occurring characters

ValueCountFrequency (%)
3190753
61.0%
82216
26.3%
221461
 
6.9%
116584
 
5.3%
41735
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number230533
73.7%
Space Separator82216
 
26.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3190753
82.7%
221461
 
9.3%
116584
 
7.2%
41735
 
0.8%
Space Separator
ValueCountFrequency (%)
82216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3190753
61.0%
82216
26.3%
221461
 
6.9%
116584
 
5.3%
41735
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3190753
61.0%
82216
26.3%
221461
 
6.9%
116584
 
5.3%
41735
 
0.6%

sidi1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
03
131360 
91417 
02
51603 
01
27860 
04
 
5234
Other values (4)
 
5275

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03
2nd row03
3rd row
4th row03
5th row03

Common Values

ValueCountFrequency (%)
03131360
42.0%
91417
29.2%
0251603
 
16.5%
0127860
 
8.9%
045234
 
1.7%
053585
 
1.1%
071287
 
0.4%
06362
 
0.1%
0041
 
< 0.1%

Length

2021-12-08T12:45:17.175868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:17.250811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
03131360
59.3%
0251603
 
23.3%
0127860
 
12.6%
045234
 
2.4%
053585
 
1.6%
071287
 
0.6%
06362
 
0.2%
0041
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0221373
35.4%
182834
29.2%
3131360
21.0%
251603
 
8.2%
127860
 
4.5%
45234
 
0.8%
53585
 
0.6%
71287
 
0.2%
6362
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number442664
70.8%
Space Separator182834
29.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0221373
50.0%
3131360
29.7%
251603
 
11.7%
127860
 
6.3%
45234
 
1.2%
53585
 
0.8%
71287
 
0.3%
6362
 
0.1%
Space Separator
ValueCountFrequency (%)
182834
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0221373
35.4%
182834
29.2%
3131360
21.0%
251603
 
8.2%
127860
 
4.5%
45234
 
0.8%
53585
 
0.6%
71287
 
0.2%
6362
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0221373
35.4%
182834
29.2%
3131360
21.0%
251603
 
8.2%
127860
 
4.5%
45234
 
0.8%
53585
 
0.6%
71287
 
0.2%
6362
 
0.1%

sidi2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
239109 
03
43841 
05
 
16028
04
 
7502
06
 
3520
Other values (3)
 
2749

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row04
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
239109
76.5%
0343841
 
14.0%
0516028
 
5.1%
047502
 
2.4%
063520
 
1.1%
072501
 
0.8%
02240
 
0.1%
018
 
< 0.1%

Length

2021-12-08T12:45:17.448654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:17.517314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0343841
59.5%
0516028
 
21.8%
047502
 
10.2%
063520
 
4.8%
072501
 
3.4%
02240
 
0.3%
018
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
478218
76.5%
073640
 
11.8%
343841
 
7.0%
516028
 
2.6%
47502
 
1.2%
63520
 
0.6%
72501
 
0.4%
2240
 
< 0.1%
18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator478218
76.5%
Decimal Number147280
 
23.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
073640
50.0%
343841
29.8%
516028
 
10.9%
47502
 
5.1%
63520
 
2.4%
72501
 
1.7%
2240
 
0.2%
18
 
< 0.1%
Space Separator
ValueCountFrequency (%)
478218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
478218
76.5%
073640
 
11.8%
343841
 
7.0%
516028
 
2.6%
47502
 
1.2%
63520
 
0.6%
72501
 
0.4%
2240
 
< 0.1%
18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
478218
76.5%
073640
 
11.8%
343841
 
7.0%
516028
 
2.6%
47502
 
1.2%
63520
 
0.6%
72501
 
0.4%
2240
 
< 0.1%
18
 
< 0.1%

sidi3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
299864 
05
 
7049
07
 
2760
06
 
1458
04
 
1426
Other values (2)
 
192

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row05
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
299864
95.9%
057049
 
2.3%
072760
 
0.9%
061458
 
0.5%
041426
 
0.5%
03185
 
0.1%
017
 
< 0.1%

Length

2021-12-08T12:45:17.715653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:17.786687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
057049
54.7%
072760
 
21.4%
061458
 
11.3%
041426
 
11.1%
03185
 
1.4%
017
 
0.1%

Most occurring characters

ValueCountFrequency (%)
599728
95.9%
012885
 
2.1%
57049
 
1.1%
72760
 
0.4%
61458
 
0.2%
41426
 
0.2%
3185
 
< 0.1%
17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator599728
95.9%
Decimal Number25770
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012885
50.0%
57049
27.4%
72760
 
10.7%
61458
 
5.7%
41426
 
5.5%
3185
 
0.7%
17
 
< 0.1%
Space Separator
ValueCountFrequency (%)
599728
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
599728
95.9%
012885
 
2.1%
57049
 
1.1%
72760
 
0.4%
61458
 
0.2%
41426
 
0.2%
3185
 
< 0.1%
17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
599728
95.9%
012885
 
2.1%
57049
 
1.1%
72760
 
0.4%
61458
 
0.2%
41426
 
0.2%
3185
 
< 0.1%
17
 
< 0.1%

sidac1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
162882 
1
119929 
2
29938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row
4th row1
5th row2

Common Values

ValueCountFrequency (%)
162882
52.1%
1119929
38.3%
229938
 
9.6%

Length

2021-12-08T12:45:17.989497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:18.055107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1119929
80.0%
229938
 
20.0%

Most occurring characters

ValueCountFrequency (%)
162882
52.1%
1119929
38.3%
229938
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator162882
52.1%
Decimal Number149867
47.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1119929
80.0%
229938
 
20.0%
Space Separator
ValueCountFrequency (%)
162882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
162882
52.1%
1119929
38.3%
229938
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
162882
52.1%
1119929
38.3%
229938
 
9.6%

sidac2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
162882 
1
117995 
2
31872 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row
4th row1
5th row2

Common Values

ValueCountFrequency (%)
162882
52.1%
1117995
37.7%
231872
 
10.2%

Length

2021-12-08T12:45:18.226780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:18.289246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1117995
78.7%
231872
 
21.3%

Most occurring characters

ValueCountFrequency (%)
162882
52.1%
1117995
37.7%
231872
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator162882
52.1%
Decimal Number149867
47.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1117995
78.7%
231872
 
21.3%
Space Separator
ValueCountFrequency (%)
162882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
162882
52.1%
1117995
37.7%
231872
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
162882
52.1%
1117995
37.7%
231872
 
10.2%

mun1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
309981 
6
 
2768

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1309981
99.1%
62768
 
0.9%

Length

2021-12-08T12:45:18.456797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:18.519357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1309981
99.1%
62768
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1309981
99.1%
62768
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number312749
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1309981
99.1%
62768
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1309981
99.1%
62768
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1309981
99.1%
62768
 
0.9%

prore1
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
310765 
28
 
243
08
 
193
15
 
91
46
 
84
Other values (48)
 
1373

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
310765
99.4%
28243
 
0.1%
08193
 
0.1%
1591
 
< 0.1%
4684
 
< 0.1%
3681
 
< 0.1%
3065
 
< 0.1%
4162
 
< 0.1%
1860
 
< 0.1%
4859
 
< 0.1%
Other values (43)1046
 
0.3%

Length

2021-12-08T12:45:18.730194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28243
 
12.2%
08193
 
9.7%
1591
 
4.6%
4684
 
4.2%
3681
 
4.1%
3065
 
3.3%
4162
 
3.1%
1860
 
3.0%
4859
 
3.0%
3157
 
2.9%
Other values (42)989
49.8%

Most occurring characters

ValueCountFrequency (%)
621530
99.4%
3611
 
0.1%
8599
 
0.1%
2538
 
0.1%
0534
 
0.1%
1486
 
0.1%
4452
 
0.1%
6233
 
< 0.1%
5212
 
< 0.1%
7174
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator621530
99.4%
Decimal Number3968
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3611
15.4%
8599
15.1%
2538
13.6%
0534
13.5%
1486
12.2%
4452
11.4%
6233
 
5.9%
5212
 
5.3%
7174
 
4.4%
9129
 
3.3%
Space Separator
ValueCountFrequency (%)
621530
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
621530
99.4%
3611
 
0.1%
8599
 
0.1%
2538
 
0.1%
0534
 
0.1%
1486
 
0.1%
4452
 
0.1%
6233
 
< 0.1%
5212
 
< 0.1%
7174
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
621530
99.4%
3611
 
0.1%
8599
 
0.1%
2538
 
0.1%
0534
 
0.1%
1486
 
0.1%
4452
 
0.1%
6233
 
< 0.1%
5212
 
< 0.1%
7174
 
< 0.1%

repaire1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
311965 
350
 
294
115
 
184
310
 
101
200
 
78
Other values (8)
 
127

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters938247
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
311965
99.7%
350294
 
0.1%
115184
 
0.1%
310101
 
< 0.1%
20078
 
< 0.1%
30044
 
< 0.1%
42025
 
< 0.1%
10020
 
< 0.1%
12814
 
< 0.1%
12511
 
< 0.1%
Other values (3)13
 
< 0.1%

Length

2021-12-08T12:45:18.938602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
350294
37.5%
115184
23.5%
310101
 
12.9%
20078
 
9.9%
30044
 
5.6%
42025
 
3.2%
10020
 
2.6%
12814
 
1.8%
12511
 
1.4%
4005
 
0.6%
Other values (2)8
 
1.0%

Most occurring characters

ValueCountFrequency (%)
935895
99.7%
0726
 
0.1%
1518
 
0.1%
5493
 
0.1%
3439
 
< 0.1%
2128
 
< 0.1%
434
 
< 0.1%
814
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator935895
99.7%
Decimal Number2352
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0726
30.9%
1518
22.0%
5493
21.0%
3439
18.7%
2128
 
5.4%
434
 
1.4%
814
 
0.6%
Space Separator
ValueCountFrequency (%)
935895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common938247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
935895
99.7%
0726
 
0.1%
1518
 
0.1%
5493
 
0.1%
3439
 
< 0.1%
2128
 
< 0.1%
434
 
< 0.1%
814
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII938247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
935895
99.7%
0726
 
0.1%
1518
 
0.1%
5493
 
0.1%
3439
 
< 0.1%
2128
 
< 0.1%
434
 
< 0.1%
814
 
< 0.1%

traant
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
312749 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters312749
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
312749
100.0%

Length

2021-12-08T12:45:19.128409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:19.191988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
312749
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator312749
100.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
312749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common312749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
312749
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII312749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312749
100.0%

aoi
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
09
119029 
04
110655 
47779 
06
17932 
03
 
9895
Other values (3)
 
7459

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04
2nd row09
3rd row
4th row03
5th row05

Common Values

ValueCountFrequency (%)
09119029
38.1%
04110655
35.4%
47779
15.3%
0617932
 
5.7%
039895
 
3.2%
083922
 
1.3%
051779
 
0.6%
071758
 
0.6%

Length

2021-12-08T12:45:19.362535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T12:45:19.432152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
09119029
44.9%
04110655
41.8%
0617932
 
6.8%
039895
 
3.7%
083922
 
1.5%
051779
 
0.7%
071758
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0264970
42.4%
9119029
19.0%
4110655
17.7%
95558
 
15.3%
617932
 
2.9%
39895
 
1.6%
83922
 
0.6%
51779
 
0.3%
71758
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number529940
84.7%
Space Separator95558
 
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0264970
50.0%
9119029
22.5%
4110655
20.9%
617932
 
3.4%
39895
 
1.9%
83922
 
0.7%
51779
 
0.3%
71758
 
0.3%
Space Separator
ValueCountFrequency (%)
95558
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0264970
42.4%
9119029
19.0%
4110655
17.7%
95558
 
15.3%
617932
 
2.9%
39895
 
1.6%
83922
 
0.6%
51779
 
0.3%
71758
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0264970
42.4%
9119029
19.0%
4110655
17.7%
95558
 
15.3%
617932
 
2.9%
39895
 
1.6%
83922
 
0.6%
51779
 
0.3%
71758
 
0.3%

cse
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
181451 
11
27093 
14
26275 
13
23079 
16
20433 
Other values (15)
34418 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters625498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16
2nd row
3rd row
4th row13
5th row

Common Values

ValueCountFrequency (%)
181451
58.0%
1127093
 
8.7%
1426275
 
8.4%
1323079
 
7.4%
1620433
 
6.5%
088916
 
2.9%
075029
 
1.6%
174589
 
1.5%
064292
 
1.4%
053581
 
1.1%
Other values (10)8011
 
2.6%

Length

2021-12-08T12:45:19.649616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1127093
20.6%
1426275
20.0%
1323079
17.6%
1620433
15.6%
088916
 
6.8%
075029
 
3.8%
174589
 
3.5%
064292
 
3.3%
053581
 
2.7%
102558
 
1.9%
Other values (9)5453
 
4.2%

Most occurring characters

ValueCountFrequency (%)
362902
58.0%
1134144
 
21.4%
027216
 
4.4%
426299
 
4.2%
624725
 
4.0%
323118
 
3.7%
89665
 
1.5%
79618
 
1.5%
54331
 
0.7%
23038
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator362902
58.0%
Decimal Number262596
42.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1134144
51.1%
027216
 
10.4%
426299
 
10.0%
624725
 
9.4%
323118
 
8.8%
89665
 
3.7%
79618
 
3.7%
54331
 
1.6%
23038
 
1.2%
9442
 
0.2%
Space Separator
ValueCountFrequency (%)
362902
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common625498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
362902
58.0%
1134144
 
21.4%
027216
 
4.4%
426299
 
4.2%
624725
 
4.0%
323118
 
3.7%
89665
 
1.5%
79618
 
1.5%
54331
 
0.7%
23038
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII625498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
362902
58.0%
1134144
 
21.4%
027216
 
4.4%
426299
 
4.2%
624725
 
4.0%
323118
 
3.7%
89665
 
1.5%
79618
 
1.5%
54331
 
0.7%
23038
 
0.5%

factorel
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41130
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.9451528
Minimum6.4000001
Maximum2550.8701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:45:19.756857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6.4000001
5-th percentile85.910004
Q1152.07001
median226.44
Q3369.17999
95-th percentile751.566006
Maximum2550.8701
Range2544.4701
Interquartile range (IQR)217.10998

Descriptive statistics

Standard deviation220.2435032
Coefficient of variation (CV)0.7367354885
Kurtosis5.340121969
Mean298.9451528
Median Absolute Deviation (MAD)90.93001
Skewness1.926995924
Sum93494797.59
Variance48507.20069
MonotonicityNot monotonic
2021-12-08T12:45:19.888731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
531.71002462
 
0.1%
550.84998414
 
0.1%
184.11363
 
0.1%
483.60001309
 
0.1%
214.03283
 
0.1%
537.92999260
 
0.1%
563.96997241
 
0.1%
294.85001240
 
0.1%
596.54999232
 
0.1%
163.60001232
 
0.1%
Other values (41120)309713
99.0%
ValueCountFrequency (%)
6.40000011
 
< 0.1%
6.55000021
 
< 0.1%
9.18000031
 
< 0.1%
9.47000031
 
< 0.1%
9.61999992
< 0.1%
10.23
< 0.1%
10.41
 
< 0.1%
10.433
< 0.1%
10.641
 
< 0.1%
10.721
 
< 0.1%
ValueCountFrequency (%)
2550.87012
< 0.1%
2550.81011
< 0.1%
2427.33012
< 0.1%
2420.90991
< 0.1%
2398.51
< 0.1%
2387.61012
< 0.1%
2380.511
< 0.1%
2375.022
< 0.1%
2366.93992
< 0.1%
2353.33011
< 0.1%

aoi_num
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing47779
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean6.429916594
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-08T12:45:19.991234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median6
Q39
95-th percentile9
Maximum9
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.436647175
Coefficient of variation (CV)0.3789547095
Kurtosis-1.880936849
Mean6.429916594
Median Absolute Deviation (MAD)2
Skewness0.0111290353
Sum1703735
Variance5.937249456
MonotonicityNot monotonic
2021-12-08T12:45:20.073010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9119029
38.1%
4110655
35.4%
617932
 
5.7%
39895
 
3.2%
83922
 
1.3%
51779
 
0.6%
71758
 
0.6%
(Missing)47779
15.3%
ValueCountFrequency (%)
39895
 
3.2%
4110655
35.4%
51779
 
0.6%
617932
 
5.7%
71758
 
0.6%
83922
 
1.3%
9119029
38.1%
ValueCountFrequency (%)
9119029
38.1%
83922
 
1.3%
71758
 
0.6%
617932
 
5.7%
51779
 
0.6%
4110655
35.4%
39895
 
3.2%

Interactions

2021-12-08T12:43:41.317623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:41.535146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:41.735886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:41.937766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:42.135512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:42.337313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:42.546142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:42.741844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:42.953439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:43.144594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:43.335393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:43.464051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:43.660754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:43.784908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:43:43.928228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-12-08T12:44:20.832142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:20.950950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.069207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.186020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.298594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.419138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.533689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.647205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.754084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.877769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:21.995694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:22.113484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:22.224309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:22.341884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:22.529446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:22.722889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:22.923708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:23.116506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:23.302765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:23.493035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:23.684601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:23.868751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:24.062624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:24.243219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:24.428948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:24.554484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:24.744307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:24.868822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:25.009835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:25.154970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:25.300884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:25.489290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:25.701101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:25.834614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:25.954105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:26.084374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:26.208601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:26.332082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:26.479145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:26.605408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:26.728832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:26.863960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:26.994185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:27.122870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:27.249921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:27.387220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:27.522566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:27.645556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:27.767196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:27.883764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:28.008888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:28.123349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:28.257636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:28.408511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:28.557345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:28.698549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:29.115819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:29.262682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:29.414098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:29.552754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:29.700553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:29.840517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:29.978799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:30.107903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:30.251167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:30.374320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:30.514220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:30.658391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:30.779902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:30.921816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:31.056985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:31.208821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:31.354747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:31.511561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:31.655467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:31.806291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:31.962131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:32.114079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:32.256855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:32.409725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:32.560494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:32.706433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:32.830981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:32.982939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:33.106356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:33.244286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:33.400336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:33.521367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:33.638261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:33.779281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:33.920956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:34.062297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:34.201070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:34.329745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:34.461833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:34.599278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:34.727145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:34.852324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:34.991683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:35.113295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:35.236037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:35.346275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:35.479662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:35.590043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:35.709725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:35.795029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:35.910414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:36.039681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:36.172838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:36.361078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:36.559784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:36.762608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:36.957420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:37.154224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:37.354505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:37.554855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:37.749691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:37.951484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:38.131749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:38.314867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:38.428175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:38.615559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:38.732027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:38.865397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:39.010713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:39.151438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:39.347232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:39.530408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:39.715534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:39.909774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:40.102519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:40.290747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:40.477007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:40.666641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:40.850800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:41.025395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:41.218128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:41.395323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:41.575816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:41.695045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:41.884171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:42.007646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:42.147602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:42.294491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:42.439791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:42.625011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-08T12:44:42.810688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-12-08T12:45:20.213768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-08T12:45:20.476150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-08T12:45:20.733902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-08T12:45:21.125549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-12-08T12:45:22.366692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-12-08T12:44:46.491455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-08T12:44:50.993273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-08T12:44:53.791179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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00018716111135112001011NaNSG18.033NaN182081117.024.00114000400040006636NaN2031110416274.230014.0
11118716111235261001011NaNSP16.033NaN6663NaNNaN660363120.03030405109274.230019.0
222187161123036012011NaNNaNNaNNaNNaNNaN1194.37000NaN
33318716121145160004011NaNSU18.033NaN1550811243.0243.001605250025002000601611406NaN2031110313203.820013.0
44418716121220310011011NaNS118.033NaN6663NaNNaN11150616NaN20322105203.820015.0
55518716121316360011011NaNS115.01SG3NaN6663NaNNaN660616NaN30103109203.820019.0
66618716131145112002011NaNSU20.033NaN12807411259.0283.0011350035001730614636NaN31110411161.320014.0
77718716131240261002420115.0SU37.033NaN12808110.067.00113000300030006636NaN31110411161.320014.0
8881871613231031012011NaNNaNNaNNaNNaNNaN1193.27000NaN
999187161324531012011NaNNaNNaNNaNNaNNaN1183.64000NaN

Last rows

Unnamed: 0Unnamed: 0.1cicloccaaprovnvivinivelnpersedad5relpp1sexo1nconynpadrenmadrerellmilieciv1prona1regna1nac1exregna1anore1nformarellb1edadestcursrncursrcursnrncurnrhcurnrrellb2traremayudfaausentrznotbvinculnuevemocup1act1situspducon1ducon2ducon3tcontmtcontddrendcomproestregestparco1parco2horasphorashhoraseextraextpagextnpgrzdifhtrapluocuplu1actplu1sitpluhorplumashordismasrzndishhordesbusotrbuscadeseafobactnbuscaasalaembusitbudisprzndisempantdtantocupaactasituaofempsidi1sidi2sidi3sidac1sidac2mun1prore1repaire1traantaoicsefactorelaoi_num
3127393127391479751915252596341325360012521NaNP213.033NaN6663NaNNaN11150816NaN222105104.700005.0
3127403127401479761915252596341420360011521NaNSG18.01SU3NaN6663NaNNaN6606616NaN301109104.700009.0
3127413127411479771915252596341525462002521NaNSU20.033NaN6663NaNNaN6104626NaN207109104.700009.0
31274231274214797819152525963426056025521NaNNaNNaNNaNNaNNaN1139.82001NaN
31274331274314797919152525963427051003521NaNNaNNaNNaNNaNNaN1148.36000NaN
3127443127441479801915252596351145162001521NaNS120.033NaN6663NaNNaN111501110.058071032210614169.610006.0
3127453127451479811915252596351245211001521NaNP212.033NaN19008603003.03.03014000400040006636NaN31110405169.610004.0
3127463127461479821915252596351316310211461NaNS116.01SG3NaN6663NaNNaN6606616NaN30103109169.610009.0
312747312747147983191525259635241031021521NaNNaNNaNNaNNaNNaN1152.44000NaN
31274831274814798419152525963525536021521NaNNaNNaNNaNNaNNaN1123.43000NaN